Datasets:

WINGNUS
/

ACL-OCL

	{
	"paper_id": "2021",
	"header": {
	"generated_with": "S2ORC 1.0.0",
	"date_generated": "2023-01-19T16:20:23.876417Z"
	},
	"title": "Sentiment Classification of Code-Mixed Tweets using Bi-Directional RNN and Language Tags",
	"authors": [
	{
	"first": "Sainik",
	"middle": [],
	"last": "Kumar Mahata",
	"suffix": "",
	"affiliation": {},
	"email": ""
	},
	{
	"first": "Dipankar",
	"middle": [],
	"last": "Das",
	"suffix": "",
	"affiliation": {},
	"email": ""
	},
	{
	"first": "Sivaji",
	"middle": [],
	"last": "Bandyopadhyay",
	"suffix": "",
	"affiliation": {
	"laboratory": "",
	"institution": "Jadavpur University",
	"location": {
	"settlement": "Kolkata",
	"country": "India"
	}
	},
	"email": ""
	}
	],
	"year": "",
	"venue": null,
	"identifiers": {},
	"abstract": "Sentiment analysis tools and models have been developed extensively throughout the years, for European languages. In contrast, similar tools for Indian Languages are scarce. This is because, state-of-the-art pre-processing tools like POS tagger, shallow parsers, etc., are not readily available for Indian languages. Although, such working tools for Indian languages, like Hindi and Bengali, that are spoken by the majority of the population, are available, finding the same for less spoken languages like, Tamil, Telugu, and Malayalam, is difficult. Moreover, due to the advent of social media, the multilingual population of India, who are comfortable with both English ad their regional language, prefer to communicate by mixing both languages. This gives rise to massive code-mixed content and automatically annotating them with their respective sentiment labels becomes a challenging task. In this work, we take up a similar challenge of developing a sentiment analysis model that can work with English-Tamil code-mixed data. The proposed work tries to solve this by using bi-directional LSTMs along with language tagging. Other traditional methods, based on classical machine learning algorithms have also been discussed in the literature, and they also act as the baseline systems to which we will compare our Neural Network based model. The performance of the developed algorithm, based on Neural Network architecture, garnered precision, recall, and F1 scores of 0.59, 0.66, and 0.58 respectively.",
	"pdf_parse": {
	"paper_id": "2021",
	"_pdf_hash": "",
	"abstract": [
	{
	"text": "Sentiment analysis tools and models have been developed extensively throughout the years, for European languages. In contrast, similar tools for Indian Languages are scarce. This is because, state-of-the-art pre-processing tools like POS tagger, shallow parsers, etc., are not readily available for Indian languages. Although, such working tools for Indian languages, like Hindi and Bengali, that are spoken by the majority of the population, are available, finding the same for less spoken languages like, Tamil, Telugu, and Malayalam, is difficult. Moreover, due to the advent of social media, the multilingual population of India, who are comfortable with both English ad their regional language, prefer to communicate by mixing both languages. This gives rise to massive code-mixed content and automatically annotating them with their respective sentiment labels becomes a challenging task. In this work, we take up a similar challenge of developing a sentiment analysis model that can work with English-Tamil code-mixed data. The proposed work tries to solve this by using bi-directional LSTMs along with language tagging. Other traditional methods, based on classical machine learning algorithms have also been discussed in the literature, and they also act as the baseline systems to which we will compare our Neural Network based model. The performance of the developed algorithm, based on Neural Network architecture, garnered precision, recall, and F1 scores of 0.59, 0.66, and 0.58 respectively.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Abstract",
	"sec_num": null
	}
	],
	"body_text": [
	{
	"text": "Sentiment analysis is the interpretation and classification of emotions (positive, negative, and neutral) within text data using text analysis techniques. It is one of the most important research areas in the domain of Natural Language Processing (NLP) and has garnered much attention in the recent past.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Introduction",
	"sec_num": "1"
	},
	{
	"text": "Throughout the years, multiple state-of-the-Art sentiment analysis models have been developed for the well known European languages, using classical Machine Learning (ML) algorithms as well as the recently developed Neural Network (NN) models. In contrast, very few such models have been developed for Indian languages, due to their lower digital footprint, which results in the lack of annotated data. Also, various pre-processing tools like Parts-of-Speech (POS) taggers, tokenizers, parsers, etc., for Indian languages, are not readily available or are not of competitive quality. Although, recent advances have been made for the Indian languages that are spoken by the majority of the population, like Hindi and Bengali, the same cannot be said for under-resourced languages such as, Tamil, Telugu, and Malayalam. For over 2600 years, recorded Tamil literature has been documented. Sangam literature, the earliest period of Tamil literature, is dated from around 600 BC-300 AD. Among the Dravidian languages, Tamil has the oldest existing literature. Tamil is the oldest living language in India.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Introduction",
	"sec_num": "1"
	},
	{
	"text": "Moreover, with the advent of social media, sentiment analysis research has become even more wide-spread Garain et al., 2020) as it takes into account conversations of customers around the social space and puts them into context. But, in the context of the Indian subcontinent, social media texts are not in one language and are largely code-mixed in nature. This is because India, has had much foreign acquaintance historically, and this has led the diaspora to adopt English as one of their official languages. Due to this, much of the Indian population are familiar with English as well as one or more regional languages (Mahata et al., 2019) . This leads to communication in sentences, which contain more than one language in the same phrase (Soumil .",
	"cite_spans": [
	{
	"start": 104,
	"end": 124,
	"text": "Garain et al., 2020)",
	"ref_id": "BIBREF24"
	},
	{
	"start": 623,
	"end": 644,
	"text": "(Mahata et al., 2019)",
	"ref_id": "BIBREF33"
	}
	],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Introduction",
	"sec_num": "1"
	},
	{
	"text": "Furthermore, in a code-mixed communication,",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Introduction",
	"sec_num": "1"
	},
	{
	"text": "words of different languages are generally written in Roman script, which leads to the formation of complex syntactical structures that are difficult to parse with traditional NLP tools. While traditional sentiment analysis models can model themselves on social media texts in one language, the same cannot be said for texts that are code-mixed in nature and also comprised of Indian low-resourced languages.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Introduction",
	"sec_num": "1"
	},
	{
	"text": "The proposed approach aims to mitigate this research problem for English-Tamil code-mixed texts and uses Bi-Directional Long-Short-term-Memory (LSTM)s (Hochreiter and Schmidhuber, 1997) to tag the texts with their respective sentiment. Language tagging of individual words was used as additional features while training the classification model. Moreover, the training corpus was passed through FastText (Bojanowski et al., 2016) embedding, to map the semantically similar words in a common 3D space. This mapping was also used to build the classification system. The designed model, when evaluated on test data, garnered an F1 score of 0.58.",
	"cite_spans": [
	{
	"start": 151,
	"end": 185,
	"text": "(Hochreiter and Schmidhuber, 1997)",
	"ref_id": "BIBREF30"
	},
	{
	"start": 404,
	"end": 429,
	"text": "(Bojanowski et al., 2016)",
	"ref_id": "BIBREF5"
	}
	],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Introduction",
	"sec_num": "1"
	},
	{
	"text": "Other baseline sentiment analysis models were also developed using classical ML algorithms and were used to compare the quality of the proposed algorithm developed by using NNs.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Introduction",
	"sec_num": "1"
	},
	{
	"text": "The rest of the paper is organized as follows. Section 2 describes some of the previous research work conducted on the domain of language identification and sentiment analysis of code-mixed texts. Section 3 describes the training and the test data used to develop and analyze our model. Section 4 introduces the model developed for identifying languages of individual words in a code-mixed sentence. Also, it describes our developed model and all the baseline models that were developed using traditional ML algorithms.Finally, section 5 and 6 deals with the evaluation of our model and the concluding remarks.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Introduction",
	"sec_num": "1"
	},
	{
	"text": "Social media has become the voice of many people over the decades and it has special relations with real-time events. With its rise, a lot of data is being generated every day and information extraction from such data has become an important research area. Also, the multi-lingual speaker, who prefers to communicate in more than one language, when expressing their opinions, generates a new kind of language, known as code-mixed language. Since, these kinds of data are more or less always written in the Roman script, analyzing these kinds of data, with help of NLP tools, becomes even more difficult.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Related Work",
	"sec_num": "2"
	},
	{
	"text": "Over the years, many experiments have been performed on code-mixed data. These include language identification, sentiment analysis, etc., to name a few. Language identification tasks have been earlier performed on various language pairs, such as Spanish-English (Negr\u00f3n Goldbarg, 2009) , French-English (Voss et al., 2014), Hindi-English (Vyas et al., 2014; Das and Gamb\u00e4ck, 2014) and Bengali-English (Das and Gamb\u00e4ck, 2014) . While these experiments were conducted with the help of dictionary word matching and ML-based algorithms such as Support Vector Machines (SVM), word-based logistic regression classifiers, and Latent Direchlet Allocation (LDA) (Blei et al., 2003) , we use more state-of-the-art deep learning approaches to achieve the same.",
	"cite_spans": [
	{
	"start": 262,
	"end": 285,
	"text": "(Negr\u00f3n Goldbarg, 2009)",
	"ref_id": "BIBREF36"
	},
	{
	"start": 338,
	"end": 357,
	"text": "(Vyas et al., 2014;",
	"ref_id": "BIBREF48"
	},
	{
	"start": 358,
	"end": 380,
	"text": "Das and Gamb\u00e4ck, 2014)",
	"ref_id": "BIBREF23"
	},
	{
	"start": 401,
	"end": 424,
	"text": "(Das and Gamb\u00e4ck, 2014)",
	"ref_id": "BIBREF23"
	},
	{
	"start": 653,
	"end": 672,
	"text": "(Blei et al., 2003)",
	"ref_id": "BIBREF4"
	}
	],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Related Work",
	"sec_num": "2"
	},
	{
	"text": "Also, sentiment analysis or opinion mining from code-mixed data is a trivial task because",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Related Work",
	"sec_num": "2"
	},
	{
	"text": "\u2022 Generally, code-mixed data is noisy in nature and requires cleaning and normalization.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Related Work",
	"sec_num": "2"
	},
	{
	"text": "\u2022 It needs several steps such as language identification and POS tagging.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Related Work",
	"sec_num": "2"
	},
	{
	"text": "\u2022 There is no sentiment annotated code-mixed lexicon available for any language pairs.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Related Work",
	"sec_num": "2"
	},
	{
	"text": "\u2022 The available code-mixed datasets are small in size to perform any unsupervised classification.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Related Work",
	"sec_num": "2"
	},
	{
	"text": "Sentiment analysis of Hindi-English code-mixed was performed by Joshi et al. (2016) which used sub-word level representations in LSTM architecture to perform it. Shalini et. al. (Shalini et al., 2018) , attempted a case-study on sentiment analysis of English-Kannada, English-Hindi, and English-Bengali texts using various machine and deep learning methods settings, like i. Doc2Vec+SVM, ii. FastText+Softmax, iii. Bi-LSTM+SoftMax and iv. CNN+SoftMax. Their reported results showed better accuracy when using deep learning methods as compared to traditional machine learning methods. Our work, on the other hand, is an amalgamation of all the methods pointed out earlier and incorporates language identification modules, the usage of FastText embeddings, and Bi-LSTM cells to develop the deep learning model.",
	"cite_spans": [
	{
	"start": 178,
	"end": 200,
	"text": "(Shalini et al., 2018)",
	"ref_id": "BIBREF40"
	}
	],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Related Work",
	"sec_num": "2"
	},
	{
	"text": "The data for building the sentiment analysis model for English-Tamil code-mixed data was collected from the \"Dravidian-CodeMix -FIRE 2020\" 1 shared task. The organizers of the task provided us with Tamil-English and Malayalam-English codemixed text data, derived from YouTube video comments. The dataset contained all the three types of code-mixed sentences -Inter-Sentential switch, Intra-Sentential switch, and Tag switching and had five output labels; Positive, Negative, Mixed Feelings, Not Tamil, and Unknown State. Most comments were written in Roman script with either Tamil / Malayalam grammar with English lexicon or English grammar with Tamil / Malayalam lexicon. Some comments were written in Tamil / Malayalam script with English expressions in between. Further, the English-Tamil dataset was divided into training, validation, and test data which had 11,335, 1,260, and 3,149 code-mixed sentence instances respectively.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Data",
	"sec_num": "3"
	},
	{
	"text": "After we collected the English-Tamil code-mixed labeled dataset, the initial pre-processing steps included the removal of extra characters to clean the data. The extra characters that were removed/cleaned included After the pre-processing step, we proceeded with tokenizing the cleaned sentences using the NLTK 2 library. Subsequently, we used this data to train FastText embedding. This was done, to map the words with similar meaning and context, close to each other in a 3D space. The skip-gram model was used instead of the continuous-bag-of-words (CBOW) model as skip-gram works best for low data sizes. The model took into account character n-grams from 3 to 6 characters. Using the trained model, we were able to extract word vectors of size 100. These word vectors were preserved to be used as input for our sentiment analysis model.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Framework",
	"sec_num": "4"
	},
	{
	"text": "Apart from providing our model, with the sequential word vectors of sentences, we also decided on providing an extra input in the form of language tags of every word of the sentences. For this, we developed a language identification system, that was trained to classify individual words, written in Roman script, as either English or Tamil. To achieve this, we used the character-level LSTM architecture put forward by . This is a model having stacked LSTM of sizes 193-128-128-1, in order where 193 is the input dimension while 1 is the output dimension.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Language Identification",
	"sec_num": "4.1"
	},
	{
	"text": "The training data was acquired by concatenating different datasets for both English and Tamil. For the English data, we used the words from the NLTK corpus, that contained 2,34,377 unique English words. For the Tamil data, we used the data from Google Dakshina Dataset 3 . This dataset contained 48,998 Tamil words, transliterated in Roman script. After adding up both the datasets, we were able to gather 2,83,332 words. Of this, 3,35,792 words were used for the training data and the rest 5,000 words were used as the test data. Also, since the data labels were imbalanced, we used the class weight feature of sklearn 4 package to assign class weights.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Language Identification",
	"sec_num": "4.1"
	},
	{
	"text": "The schematic of the developed language identification model is shown in Figure 1 . After testing the model with 5,000 words, the model returned an accuracy of 96.89%. The other metrics for the model are shown in Table 1 ",
	"cite_spans": [],
	"ref_spans": [
	{
	"start": 73,
	"end": 81,
	"text": "Figure 1",
	"ref_id": "FIGREF0"
	},
	{
	"start": 213,
	"end": 220,
	"text": "Table 1",
	"ref_id": "TABREF2"
	}
	],
	"eq_spans": [],
	"section": "Language Identification",
	"sec_num": "4.1"
	},
	{
	"text": "Using the language identification model, we were able to classify the words of the validation and the training data into either English or Tamil. Now, the next step was to develop the sentiment classification model which was to be designed for taking two inputs; i. the individual words of the code-mixed tweets and ii. the language tags of the individual words in the code-mixed tweets. The vectors of the individual words of the training data, as discussed earlier, were extracted from the already trained FastText embedding file. Thereafter, vectors of sentences of the train and validation dataset were extracted from the trained embedding. The language tags and the word vectors were merged using a Concatenation layer and were given as input to a Bi-Directional LSTM cell. The context vector was then mapped to the output labels with the help of a Dense layer.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Sentiment Classification",
	"sec_num": "4.2"
	},
	{
	"text": "The schematic of the model is shown in Figure 2 .",
	"cite_spans": [],
	"ref_spans": [
	{
	"start": 39,
	"end": 47,
	"text": "Figure 2",
	"ref_id": "FIGREF1"
	}
	],
	"eq_spans": [],
	"section": "Sentiment Classification",
	"sec_num": "4.2"
	},
	{
	"text": "Other parameters of the model are as follows.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Sentiment Classification",
	"sec_num": "4.2"
	},
	{
	"text": "\u2022 batch size: 32",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Sentiment Classification",
	"sec_num": "4.2"
	},
	{
	"text": "\u2022 epochs: 50",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Sentiment Classification",
	"sec_num": "4.2"
	},
	{
	"text": "\u2022 optimizer: adam accuracy and F1-Score of 70.42% and 0.63 respectively. We also trained three other models, where the basic architecture was the same, the difference being the usage of LSTM/Bi-Directional LSTM and language tag features. The models were",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Sentiment Classification",
	"sec_num": "4.2"
	},
	{
	"text": "\u2022 Bidirectional LSTM without the language tag feature.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Sentiment Classification",
	"sec_num": "4.2"
	},
	{
	"text": "\u2022 LSTM with the language tag feature.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Sentiment Classification",
	"sec_num": "4.2"
	},
	{
	"text": "\u2022 LSTM without the language tag feature.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Sentiment Classification",
	"sec_num": "4.2"
	},
	{
	"text": "The accuracy and F1-Score of every model are shown in Table 2 .",
	"cite_spans": [],
	"ref_spans": [
	{
	"start": 54,
	"end": 61,
	"text": "Table 2",
	"ref_id": "TABREF5"
	}
	],
	"eq_spans": [],
	"section": "Sentiment Classification",
	"sec_num": "4.2"
	},
	{
	"text": "For developing the baseline models, we decided on using traditional ML algorithms. The algorithms chosen were, matrix of token counts was used as a feature. This implementation produces a sparse representation of the counts. Since we did not provide an a-priori dictionary and did not use an analyzer that does some kind of feature selection, the number of features was equal to the vocabulary size found by analyzing the data. For the second model, we used the TF-IDF Vectorizer, with maximum features of 5000, where it converts a collection of raw documents to a matrix of TF-IDF features. We used the 2-gram and 3-gram range for this.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Baseline Models",
	"sec_num": "4.3"
	},
	{
	"text": "Also, for the third and the fourth model, the same features, Count Vectorizer and TF-IDF Vectorizer were used but in this case, we went for data augmentation, where the input was changed from words only to the form of W ord LanguageT ag.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Baseline Models",
	"sec_num": "4.3"
	},
	{
	"text": "On validation, the accuracy metrics garnered by the developed models, are shown in Table 3 .",
	"cite_spans": [],
	"ref_spans": [
	{
	"start": 83,
	"end": 90,
	"text": "Table 3",
	"ref_id": "TABREF6"
	}
	],
	"eq_spans": [],
	"section": "Baseline Models",
	"sec_num": "4.3"
	},
	{
	"text": "From Tables 2 and 3, we can see that though the ML and DL models perform neck-in-neck, but still, we preferred the DL model, developed using Bidirectional LSTM's and language tag feature as it garnered the highest F1-Score. This model was then tested using 3,149 test data, provided by the shared task organizers. The results of the testing phase of the selected model are quantified in Table 4 .",
	"cite_spans": [],
	"ref_spans": [
	{
	"start": 387,
	"end": 395,
	"text": "Table 4",
	"ref_id": "TABREF7"
	}
	],
	"eq_spans": [],
	"section": "Evaluation",
	"sec_num": "5"
	},
	{
	"text": "Precision Recall F1-Score Bi-LSTM+ ln tag 0.59 0.66 0.58 ",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Model",
	"sec_num": null
	},
	{
	"text": "In the current work, we attempted to solve the problem of Sentiment Analysis of code-mixed English-Tamil sentences. Our system was based on using Bi-Directional LSTM along with Language Tag features. Also, FastText embedding was used to generate word vectors to train the model. For predicting the language tags, another deep learning system, based on character embedding was also developed. Other models, based on traditional ML algorithms were also developed that was used to compare our developed model. Our system, when evaluated on the test data, garnered an F1 score of 0.58. As future work, we would like to increase this data, as deep learning algorithms tend to work well with higher amount of data and use state-ofthe-art Neural Network architectures, like BERT, RoBERTa, etc., on this data, taking into advantage the concept of matrix and embedded language, Sen-tiWordNet, and other NLP features.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Conclusion",
	"sec_num": "6"
	},
	{
	"text": "https://dravidian-codemix.github.io/ 2020/ 2 https://www.nltk.org/",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "",
	"sec_num": null
	},
	{
	"text": "https://github.com/ google-research-datasets/dakshina 4 https://scikit-learn.org/stable/ modules/generated/sklearn.utils.\\class_ weight.compute_class_weight.html",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "",
	"sec_num": null
	}
	],
	"back_matter": [],
	"bib_entries": {
	"BIBREF0": {
	"ref_id": "b0",
	"title": "The Theory of Parsing",
	"authors": [
	{
	"first": "Alfred",
	"middle": [
	"V"
	],
	"last": "Aho",
	"suffix": ""
	},
	{
	"first": "Jeffrey",
	"middle": [
	"D"
	],
	"last": "Ullman",
	"suffix": ""
	}
	],
	"year": 1972,
	"venue": "Translation and Compiling",
	"volume": "1",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Alfred V. Aho and Jeffrey D. Ullman. 1972. The Theory of Parsing, Translation and Compiling, vol- ume 1. Prentice-Hall, Englewood Cliffs, NJ.",
	"links": null
	},
	"BIBREF1": {
	"ref_id": "b1",
	"title": "Publications Manual",
	"authors": [],
	"year": 1983,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "American Psychological Association. 1983. Publica- tions Manual. American Psychological Association, Washington, DC.",
	"links": null
	},
	"BIBREF2": {
	"ref_id": "b2",
	"title": "A framework for learning predictive structures from multiple tasks and unlabeled data",
	"authors": [
	{
	"first": "Rie",
	"middle": [],
	"last": "Kubota",
	"suffix": ""
	},
	{
	"first": "Ando",
	"middle": [],
	"last": "",
	"suffix": ""
	},
	{
	"first": "Tong",
	"middle": [],
	"last": "Zhang",
	"suffix": ""
	}
	],
	"year": 2005,
	"venue": "Journal of Machine Learning Research",
	"volume": "6",
	"issue": "",
	"pages": "1817--1853",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Rie Kubota Ando and Tong Zhang. 2005. A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research, 6:1817-1853.",
	"links": null
	},
	"BIBREF3": {
	"ref_id": "b3",
	"title": "Scalable training of L1-regularized log-linear models",
	"authors": [
	{
	"first": "Galen",
	"middle": [],
	"last": "Andrew",
	"suffix": ""
	},
	{
	"first": "Jianfeng",
	"middle": [],
	"last": "Gao",
	"suffix": ""
	}
	],
	"year": 2007,
	"venue": "Proceedings of the 24th International Conference on Machine Learning",
	"volume": "",
	"issue": "",
	"pages": "33--40",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Galen Andrew and Jianfeng Gao. 2007. Scalable train- ing of L1-regularized log-linear models. In Proceed- ings of the 24th International Conference on Ma- chine Learning, pages 33-40.",
	"links": null
	},
	"BIBREF4": {
	"ref_id": "b4",
	"title": "Latent dirichlet allocation",
	"authors": [
	{
	"first": "M",
	"middle": [],
	"last": "David",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Blei",
	"suffix": ""
	},
	{
	"first": "Y",
	"middle": [],
	"last": "Andrew",
	"suffix": ""
	},
	{
	"first": "Michael I Jordan",
	"middle": [],
	"last": "Ng",
	"suffix": ""
	}
	],
	"year": 2003,
	"venue": "Journal of machine Learning research",
	"volume": "3",
	"issue": "",
	"pages": "993--1022",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. Journal of ma- chine Learning research, 3(Jan):993-1022.",
	"links": null
	},
	"BIBREF5": {
	"ref_id": "b5",
	"title": "Enriching word vectors with subword information",
	"authors": [
	{
	"first": "Piotr",
	"middle": [],
	"last": "Bojanowski",
	"suffix": ""
	},
	{
	"first": "Edouard",
	"middle": [],
	"last": "Grave",
	"suffix": ""
	},
	{
	"first": "Armand",
	"middle": [],
	"last": "Joulin",
	"suffix": ""
	},
	{
	"first": "Tomas",
	"middle": [],
	"last": "Mikolov",
	"suffix": ""
	}
	],
	"year": 2016,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {
	"arXiv": [
	"arXiv:1607.04606"
	]
	},
	"num": null,
	"urls": [],
	"raw_text": "Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2016. Enriching word vec- tors with subword information. arXiv preprint arXiv:1607.04606.",
	"links": null
	},
	"BIBREF6": {
	"ref_id": "b6",
	"title": "HopeEDI: A multilingual hope speech detection dataset for equality, diversity, and inclusion",
	"authors": [
	{
	"first": "Chakravarthi",
	"middle": [],
	"last": "Bharathi Raja",
	"suffix": ""
	}
	],
	"year": 2020,
	"venue": "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
	"volume": "",
	"issue": "",
	"pages": "41--53",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Bharathi Raja Chakravarthi. 2020a. HopeEDI: A mul- tilingual hope speech detection dataset for equality, diversity, and inclusion. In Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Me- dia, pages 41-53, Barcelona, Spain (Online). Asso- ciation for Computational Linguistics.",
	"links": null
	},
	"BIBREF7": {
	"ref_id": "b7",
	"title": "Leveraging orthographic information to improve machine translation of under-resourced languages",
	"authors": [
	{
	"first": "Chakravarthi",
	"middle": [],
	"last": "Bharathi Raja",
	"suffix": ""
	}
	],
	"year": 2020,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Bharathi Raja Chakravarthi. 2020b. Leveraging ortho- graphic information to improve machine translation of under-resourced languages. Ph.D. thesis, NUI Galway.",
	"links": null
	},
	"BIBREF8": {
	"ref_id": "b8",
	"title": "Improving wordnets for underresourced languages using machine translation",
	"authors": [
	{
	"first": "Mihael",
	"middle": [],
	"last": "Bharathi Raja Chakravarthi",
	"suffix": ""
	},
	{
	"first": "John",
	"middle": [
	"P"
	],
	"last": "Arcan",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Mccrae",
	"suffix": ""
	}
	],
	"year": 2018,
	"venue": "Proceedings of the 9th Global Wordnet Conference",
	"volume": "",
	"issue": "",
	"pages": "77--86",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Bharathi Raja Chakravarthi, Mihael Arcan, and John P. McCrae. 2018. Improving wordnets for under- resourced languages using machine translation. In Proceedings of the 9th Global Wordnet Conference, pages 77-86, Nanyang Technological University (NTU), Singapore. Global Wordnet Association.",
	"links": null
	},
	"BIBREF10": {
	"ref_id": "b10",
	"title": "WordNet gloss translation for underresourced languages using multilingual neural machine translation",
	"authors": [
	{
	"first": "",
	"middle": [],
	"last": "Mccrae",
	"suffix": ""
	}
	],
	"year": 2019,
	"venue": "Proceedings of the Second Workshop on Multilingualism at the Intersection of Knowledge Bases and Machine Translation",
	"volume": "",
	"issue": "",
	"pages": "1--7",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "McCrae. 2019. WordNet gloss translation for under- resourced languages using multilingual neural ma- chine translation. In Proceedings of the Second Workshop on Multilingualism at the Intersection of Knowledge Bases and Machine Translation, pages 1-7, Dublin, Ireland. European Association for Ma- chine Translation.",
	"links": null
	},
	"BIBREF11": {
	"ref_id": "b11",
	"title": "A sentiment analysis dataset for codemixed Malayalam-English",
	"authors": [
	{
	"first": "Navya",
	"middle": [],
	"last": "Bharathi Raja Chakravarthi",
	"suffix": ""
	},
	{
	"first": "Shardul",
	"middle": [],
	"last": "Jose",
	"suffix": ""
	},
	{
	"first": "Elizabeth",
	"middle": [],
	"last": "Suryawanshi",
	"suffix": ""
	},
	{
	"first": "John",
	"middle": [
	"Philip"
	],
	"last": "Sherly",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Mc-Crae",
	"suffix": ""
	}
	],
	"year": 2020,
	"venue": "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
	"volume": "",
	"issue": "",
	"pages": "177--184",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Bharathi Raja Chakravarthi, Navya Jose, Shardul Suryawanshi, Elizabeth Sherly, and John Philip Mc- Crae. 2020a. A sentiment analysis dataset for code- mixed Malayalam-English. In Proceedings of the 1st Joint Workshop on Spoken Language Technolo- gies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL), pages 177-184, Marseille, France. European Language Resources association.",
	"links": null
	},
	"BIBREF12": {
	"ref_id": "b12",
	"title": "Overview of the track on HASOC-Offensive Language Identification-DravidianCodeMix",
	"authors": [
	{
	"first": "Anand",
	"middle": [],
	"last": "Bharathi Raja Chakravarthi",
	"suffix": ""
	},
	{
	"first": "John",
	"middle": [],
	"last": "Kumar",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Philip Mccrae",
	"suffix": ""
	},
	{
	"first": "B",
	"middle": [],
	"last": "Premjith",
	"suffix": ""
	},
	{
	"first": "K",
	"middle": [
	"P"
	],
	"last": "Soman",
	"suffix": ""
	},
	{
	"first": "Thomas",
	"middle": [],
	"last": "Mandl",
	"suffix": ""
	}
	],
	"year": 2020,
	"venue": "Working Notes of the Forum for Information Retrieval Evaluation",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Bharathi Raja Chakravarthi, M Anand Kumar, John Philip McCrae, Premjith B, Soman KP, and Thomas Mandl. 2020b. Overview of the track on HASOC-Offensive Language Identification- DravidianCodeMix. In Working Notes of the Forum for Information Retrieval Evaluation (FIRE 2020).",
	"links": null
	},
	"BIBREF13": {
	"ref_id": "b13",
	"title": "CEUR Workshop Proceedings",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "CEUR Workshop Proceedings. In: CEUR-WS. org, Hyderabad, India.",
	"links": null
	},
	"BIBREF14": {
	"ref_id": "b14",
	"title": "Findings of the shared task on Hope Speech Detection for Equality, Diversity, and Inclusion",
	"authors": [
	{
	"first": "Vigneshwaran",
	"middle": [],
	"last": "Bharathi Raja Chakravarthi",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Muralidaran",
	"suffix": ""
	}
	],
	"year": 2021,
	"venue": "Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Bharathi Raja Chakravarthi and Vigneshwaran Mural- idaran. 2021. Findings of the shared task on Hope Speech Detection for Equality, Diversity, and Inclu- sion. In Proceedings of the First Workshop on Lan- guage Technology for Equality, Diversity and Inclu- sion. Association for Computational Linguistics.",
	"links": null
	},
	"BIBREF15": {
	"ref_id": "b15",
	"title": "Corpus creation for sentiment analysis in code-mixed Tamil-English text",
	"authors": [
	{
	"first": "Vigneshwaran",
	"middle": [],
	"last": "Bharathi Raja Chakravarthi",
	"suffix": ""
	},
	{
	"first": "Ruba",
	"middle": [],
	"last": "Muralidaran",
	"suffix": ""
	},
	{
	"first": "John",
	"middle": [
	"Philip"
	],
	"last": "Priyadharshini",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Mc-Crae",
	"suffix": ""
	}
	],
	"year": 2020,
	"venue": "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
	"volume": "",
	"issue": "",
	"pages": "202--210",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Bharathi Raja Chakravarthi, Vigneshwaran Murali- daran, Ruba Priyadharshini, and John Philip Mc- Crae. 2020c. Corpus creation for sentiment anal- ysis in code-mixed Tamil-English text. In Pro- ceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced lan- guages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL), pages 202-210, Marseille, France. European Language Re- sources association.",
	"links": null
	},
	"BIBREF16": {
	"ref_id": "b16",
	"title": "Findings of the shared task on Machine Translation in Dravidian languages",
	"authors": [
	{
	"first": "Ruba",
	"middle": [],
	"last": "Bharathi Raja Chakravarthi",
	"suffix": ""
	},
	{
	"first": "Shubhanker",
	"middle": [],
	"last": "Priyadharshini",
	"suffix": ""
	},
	{
	"first": "Richard",
	"middle": [],
	"last": "Banerjee",
	"suffix": ""
	},
	{
	"first": "John",
	"middle": [],
	"last": "Saldhana",
	"suffix": ""
	},
	{
	"first": "Anand",
	"middle": [],
	"last": "Philip Mccrae",
	"suffix": ""
	},
	{
	"first": "M",
	"middle": [],
	"last": "Kumar",
	"suffix": ""
	},
	{
	"first": "Parameswari",
	"middle": [],
	"last": "Krishnamurthy",
	"suffix": ""
	},
	{
	"first": "Melvin",
	"middle": [],
	"last": "Johnson",
	"suffix": ""
	}
	],
	"year": 2021,
	"venue": "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Bharathi Raja Chakravarthi, Ruba Priyadharshini, Shubhanker Banerjee, Richard Saldhana, John Philip McCrae, Anand Kumar M, Parameswari Krishnamurthy, and Melvin Johnson. 2021a. Find- ings of the shared task on Machine Translation in Dravidian languages. In Proceedings of the First Workshop on Speech and Language Tech- nologies for Dravidian Languages. Association for Computational Linguistics.",
	"links": null
	},
	"BIBREF17": {
	"ref_id": "b17",
	"title": "Findings of the shared task on Offensive Language Identification in Tamil, Malayalam, and Kannada",
	"authors": [
	{
	"first": "Ruba",
	"middle": [],
	"last": "Bharathi Raja Chakravarthi",
	"suffix": ""
	},
	{
	"first": "Navya",
	"middle": [],
	"last": "Priyadharshini",
	"suffix": ""
	},
	{
	"first": "Anand",
	"middle": [],
	"last": "Jose",
	"suffix": ""
	},
	{
	"first": "M",
	"middle": [],
	"last": "Kumar",
	"suffix": ""
	},
	{
	"first": "Thomas",
	"middle": [],
	"last": "Mandl",
	"suffix": ""
	},
	{
	"first": "Prasanna",
	"middle": [],
	"last": "Kumar Kumaresan",
	"suffix": ""
	},
	{
	"first": "Rahul",
	"middle": [],
	"last": "Ponnusamy",
	"suffix": ""
	},
	{
	"first": "V",
	"middle": [],
	"last": "Hariharan",
	"suffix": ""
	},
	{
	"first": "Elizabeth",
	"middle": [],
	"last": "Sherly",
	"suffix": ""
	},
	{
	"first": "John",
	"middle": [
	"Philip"
	],
	"last": "Mc-Crae",
	"suffix": ""
	}
	],
	"year": 2021,
	"venue": "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages. Association for Computational Linguistics",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Bharathi Raja Chakravarthi, Ruba Priyadharshini, Navya Jose, Anand Kumar M, Thomas Mandl, Prasanna Kumar Kumaresan, Rahul Ponnusamy, Hariharan V, Elizabeth Sherly, and John Philip Mc- Crae. 2021b. Findings of the shared task on Offen- sive Language Identification in Tamil, Malayalam, and Kannada. In Proceedings of the First Workshop on Speech and Language Technologies for Dravid- ian Languages. Association for Computational Lin- guistics.",
	"links": null
	},
	"BIBREF18": {
	"ref_id": "b18",
	"title": "Overview of the Track on Sentiment Analysis for Dravidian Languages in Code-Mixed Text",
	"authors": [
	{
	"first": "Ruba",
	"middle": [],
	"last": "Bharathi Raja Chakravarthi",
	"suffix": ""
	},
	{
	"first": "Vigneshwaran",
	"middle": [],
	"last": "Priyadharshini",
	"suffix": ""
	},
	{
	"first": "Shardul",
	"middle": [],
	"last": "Muralidaran",
	"suffix": ""
	},
	{
	"first": "Navya",
	"middle": [],
	"last": "Suryawanshi",
	"suffix": ""
	},
	{
	"first": "Elizabeth",
	"middle": [],
	"last": "Jose",
	"suffix": ""
	},
	{
	"first": "John",
	"middle": [
	"P"
	],
	"last": "Sherly",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Mccrae",
	"suffix": ""
	}
	],
	"year": 2020,
	"venue": "In Forum for Information Retrieval Evaluation",
	"volume": "2020",
	"issue": "",
	"pages": "21--24",
	"other_ids": {
	"DOI": [
	"10.1145/3441501.3441515"
	]
	},
	"num": null,
	"urls": [],
	"raw_text": "Bharathi Raja Chakravarthi, Ruba Priyadharshini, Vigneshwaran Muralidaran, Shardul Suryawanshi, Navya Jose, Elizabeth Sherly, and John P. McCrae. 2020d. Overview of the Track on Sentiment Analy- sis for Dravidian Languages in Code-Mixed Text. In Forum for Information Retrieval Evaluation, FIRE 2020, page 21-24, New York, NY, USA. Associa- tion for Computing Machinery.",
	"links": null
	},
	"BIBREF19": {
	"ref_id": "b19",
	"title": "Overview of the track on Sentiment Analysis for Dravidian Languages in Code-Mixed Text",
	"authors": [
	{
	"first": "Ruba",
	"middle": [],
	"last": "Bharathi Raja Chakravarthi",
	"suffix": ""
	},
	{
	"first": "Vigneshwaran",
	"middle": [],
	"last": "Priyadharshini",
	"suffix": ""
	},
	{
	"first": "Shardul",
	"middle": [],
	"last": "Muralidaran",
	"suffix": ""
	},
	{
	"first": "Navya",
	"middle": [],
	"last": "Suryawanshi",
	"suffix": ""
	},
	{
	"first": "Elizabeth",
	"middle": [],
	"last": "Jose",
	"suffix": ""
	},
	{
	"first": "John",
	"middle": [
	"Philip"
	],
	"last": "Sherly",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Mc-Crae",
	"suffix": ""
	}
	],
	"year": 2020,
	"venue": "Proceedings of the 12th Forum for Information Retrieval Evaluation",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Bharathi Raja Chakravarthi, Ruba Priyadharshini, Vigneshwaran Muralidaran, Shardul Suryawanshi, Navya Jose, Elizabeth Sherly, and John Philip Mc- Crae. 2020e. Overview of the track on Sentiment Analysis for Dravidian Languages in Code-Mixed Text. In Proceedings of the 12th Forum for Infor- mation Retrieval Evaluation, FIRE '20.",
	"links": null
	},
	"BIBREF20": {
	"ref_id": "b20",
	"title": "Overview of the track on Sentiment Analysis for Dravidian Languages in Code-Mixed Text",
	"authors": [
	{
	"first": "Ruba",
	"middle": [],
	"last": "Bharathi Raja Chakravarthi",
	"suffix": ""
	},
	{
	"first": "Vigneshwaran",
	"middle": [],
	"last": "Priyadharshini",
	"suffix": ""
	},
	{
	"first": "Shardul",
	"middle": [],
	"last": "Muralidaran",
	"suffix": ""
	},
	{
	"first": "Navya",
	"middle": [],
	"last": "Suryawanshi",
	"suffix": ""
	},
	{
	"first": "John",
	"middle": [
	"Philip"
	],
	"last": "Jose",
	"suffix": ""
	},
	{
	"first": "Elizabeth",
	"middle": [],
	"last": "Sherly",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Mc-Crae",
	"suffix": ""
	}
	],
	"year": 2020,
	"venue": "Working Notes of the Forum for Information Retrieval Evaluation (FIRE 2020",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Bharathi Raja Chakravarthi, Ruba Priyadharshini, Vigneshwaran Muralidaran, Shardul Suryawanshi, Navya Jose, and John Philip Sherly, Elizabeth Mc- Crae. 2020f. Overview of the track on Sentiment Analysis for Dravidian Languages in Code-Mixed Text. In Working Notes of the Forum for Information Retrieval Evaluation (FIRE 2020). CEUR Workshop Proceedings. In: CEUR-WS. org, Hyderabad, India.",
	"links": null
	},
	"BIBREF21": {
	"ref_id": "b21",
	"title": "Bilingual lexicon induction across orthographicallydistinct under-resourced Dravidian languages",
	"authors": [
	{
	"first": "Navaneethan",
	"middle": [],
	"last": "Bharathi Raja Chakravarthi",
	"suffix": ""
	},
	{
	"first": "Mihael",
	"middle": [],
	"last": "Rajasekaran",
	"suffix": ""
	},
	{
	"first": "Kevin",
	"middle": [],
	"last": "Arcan",
	"suffix": ""
	},
	{
	"first": "Noel",
	"middle": [
	"E"
	],
	"last": "Mcguinness",
	"suffix": ""
	},
	{
	"first": "John",
	"middle": [
	"P"
	],
	"last": "O'connor",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Mccrae",
	"suffix": ""
	}
	],
	"year": 2020,
	"venue": "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
	"volume": "",
	"issue": "",
	"pages": "57--69",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Bharathi Raja Chakravarthi, Navaneethan Ra- jasekaran, Mihael Arcan, Kevin McGuinness, Noel E. O'Connor, and John P. McCrae. 2020g. Bilingual lexicon induction across orthographically- distinct under-resourced Dravidian languages. In Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects, pages 57-69, Barcelona, Spain (Online). International Committee on Computational Linguistics (ICCL).",
	"links": null
	},
	"BIBREF23": {
	"ref_id": "b23",
	"title": "Identifying languages at the word level in code-mixed indian social media text",
	"authors": [
	{
	"first": "Amitava",
	"middle": [],
	"last": "Das",
	"suffix": ""
	},
	{
	"first": "Bj\u00f6rn",
	"middle": [],
	"last": "Gamb\u00e4ck",
	"suffix": ""
	}
	],
	"year": 2014,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Amitava Das and Bj\u00f6rn Gamb\u00e4ck. 2014. Identifying languages at the word level in code-mixed indian so- cial media text.",
	"links": null
	},
	"BIBREF24": {
	"ref_id": "b24",
	"title": "JUNLP at SemEval-2020 task 9:Sentiment Analysis of Hindi-English code mixed data using Grid Search Cross Validation",
	"authors": [
	{
	"first": "Avishek",
	"middle": [],
	"last": "Garain",
	"suffix": ""
	},
	{
	"first": "Sainik",
	"middle": [],
	"last": "Kumar Mahata",
	"suffix": ""
	},
	{
	"first": "Dipankar",
	"middle": [],
	"last": "Das",
	"suffix": ""
	}
	],
	"year": 2020,
	"venue": "Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval-2020)",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Avishek Garain, Sainik Kumar Mahata, and Dipankar Das. 2020. JUNLP at SemEval-2020 task 9:Senti- ment Analysis of Hindi-English code mixed data us- ing Grid Search Cross Validation. In Proceedings of the 14th International Workshop on Semantic Eval- uation (SemEval-2020), Barcelona, Spain. Associa- tion for Computational Linguistics.",
	"links": null
	},
	"BIBREF25": {
	"ref_id": "b25",
	"title": "Ruba Priyadharshini, and Bharathi Raja Chakravarthi. 2021a. IIITK@DravidianLangTech-EACL2021: Offensive Language Identification and Meme Classification in Tamil, Malayalam and Kannada",
	"authors": [
	{
	"first": "Parameswari",
	"middle": [],
	"last": "Nikhil Kumar Ghanghor",
	"suffix": ""
	},
	{
	"first": "Sajeetha",
	"middle": [],
	"last": "Krishnamurthy",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Thavareesan",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Nikhil Kumar Ghanghor, Parameswari Krishna- murthy, Sajeetha Thavareesan, Ruba Priyad- harshini, and Bharathi Raja Chakravarthi. 2021a. IIITK@DravidianLangTech-EACL2021: Offensive Language Identification and Meme Classification in Tamil, Malayalam and Kannada. In Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages, Online. Association for Computational Linguistics.",
	"links": null
	},
	"BIBREF26": {
	"ref_id": "b26",
	"title": "IIITK@LT-EDI-EACL2021: Hope Speech Detection for Equality, Diversity, and Inclusion in Tamil, Malayalam and English",
	"authors": [
	{
	"first": "Rahul",
	"middle": [],
	"last": "Nikhil Kumar Ghanghor",
	"suffix": ""
	},
	{
	"first": "Prasanna",
	"middle": [],
	"last": "Ponnusamy",
	"suffix": ""
	},
	{
	"first": "Ruba",
	"middle": [],
	"last": "Kumar Kumaresan",
	"suffix": ""
	},
	{
	"first": "Sajeetha",
	"middle": [],
	"last": "Priyadharshini",
	"suffix": ""
	},
	{
	"first": "Bharathi Raja",
	"middle": [],
	"last": "Thavareesan",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Chakravarthi",
	"suffix": ""
	}
	],
	"year": 2021,
	"venue": "Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Nikhil Kumar Ghanghor, Rahul Ponnusamy, Prasanna Kumar Kumaresan, Ruba Priyad- harshini, Sajeetha Thavareesan, and Bharathi Raja Chakravarthi. 2021b. IIITK@LT-EDI-EACL2021: Hope Speech Detection for Equality, Diversity, and Inclusion in Tamil, Malayalam and English. In Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion, Online.",
	"links": null
	},
	"BIBREF27": {
	"ref_id": "b27",
	"title": "Algorithms on Strings, Trees and Sequences",
	"authors": [
	{
	"first": "Dan",
	"middle": [],
	"last": "Gusfield",
	"suffix": ""
	}
	],
	"year": 1997,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Dan Gusfield. 1997. Algorithms on Strings, Trees and Sequences. Cambridge University Press, Cam- bridge, UK.",
	"links": null
	},
	"BIBREF28": {
	"ref_id": "b28",
	"title": "KanCMD: Kannada CodeMixed dataset for sentiment analysis and offensive language detection",
	"authors": [
	{
	"first": "Adeep",
	"middle": [],
	"last": "Hande",
	"suffix": ""
	},
	{
	"first": "Ruba",
	"middle": [],
	"last": "Priyadharshini",
	"suffix": ""
	},
	{
	"first": "Bharathi Raja",
	"middle": [],
	"last": "Chakravarthi",
	"suffix": ""
	}
	],
	"year": 2020,
	"venue": "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
	"volume": "",
	"issue": "",
	"pages": "54--63",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Adeep Hande, Ruba Priyadharshini, and Bharathi Raja Chakravarthi. 2020. KanCMD: Kannada CodeMixed dataset for sentiment analysis and offensive language detection. In Proceedings of the Third Workshop on Computational Modeling of Peo- ple's Opinions, Personality, and Emotion's in Social Media, pages 54-63, Barcelona, Spain (Online). Association for Computational Linguistics.",
	"links": null
	},
	"BIBREF29": {
	"ref_id": "b29",
	"title": "UVCE-IIITT@DravidianLangTech-EACL2021: Tamil Troll Meme Classification: You need to Pay more Attention",
	"authors": [
	{
	"first": "Adeep",
	"middle": [],
	"last": "Siddhanth U Hegde",
	"suffix": ""
	},
	{
	"first": "Ruba",
	"middle": [],
	"last": "Hande",
	"suffix": ""
	},
	{
	"first": "Sajeetha",
	"middle": [],
	"last": "Priyadharshini",
	"suffix": ""
	},
	{
	"first": "Bharathi Raja",
	"middle": [],
	"last": "Thavareesan",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Chakravarthi",
	"suffix": ""
	}
	],
	"year": 2021,
	"venue": "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Siddhanth U Hegde, Adeep Hande, Ruba Priyadharshini, Sajeetha Thavareesan, and Bharathi Raja Chakravarthi. 2021. UVCE- IIITT@DravidianLangTech-EACL2021: Tamil Troll Meme Classification: You need to Pay more Attention. In Proceedings of the First Workshop on Speech and Language Technologies for Dra- vidian Languages. Association for Computational Linguistics.",
	"links": null
	},
	"BIBREF30": {
	"ref_id": "b30",
	"title": "Long short-term memory",
	"authors": [
	{
	"first": "Sepp",
	"middle": [],
	"last": "Hochreiter",
	"suffix": ""
	},
	{
	"first": "J\u00fcrgen",
	"middle": [],
	"last": "Schmidhuber",
	"suffix": ""
	}
	],
	"year": 1997,
	"venue": "Neural Comput",
	"volume": "9",
	"issue": "8",
	"pages": "1735--1780",
	"other_ids": {
	"DOI": [
	"10.1162/neco.1997.9.8.1735"
	]
	},
	"num": null,
	"urls": [],
	"raw_text": "Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural Comput., 9(8):1735-1780.",
	"links": null
	},
	"BIBREF31": {
	"ref_id": "b31",
	"title": "A Survey of Current Datasets for Code-Switching Research",
	"authors": [
	{
	"first": "Navya",
	"middle": [],
	"last": "Jose",
	"suffix": ""
	},
	{
	"first": "Shardul",
	"middle": [],
	"last": "Bharathi Raja Chakravarthi",
	"suffix": ""
	},
	{
	"first": "Elizabeth",
	"middle": [],
	"last": "Suryawanshi",
	"suffix": ""
	},
	{
	"first": "John",
	"middle": [
	"P"
	],
	"last": "Sherly",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Mccrae",
	"suffix": ""
	}
	],
	"year": 2020,
	"venue": "2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS)",
	"volume": "",
	"issue": "",
	"pages": "136--141",
	"other_ids": {
	"DOI": [
	"10.1109/ICACCS48705.2020.9074205"
	]
	},
	"num": null,
	"urls": [],
	"raw_text": "Navya Jose, Bharathi Raja Chakravarthi, Shardul Suryawanshi, Elizabeth Sherly, and John P. McCrae. 2020. A Survey of Current Datasets for Code- Switching Research. In 2020 6th International Con- ference on Advanced Computing and Communica- tion Systems (ICACCS), pages 136-141.",
	"links": null
	},
	"BIBREF32": {
	"ref_id": "b32",
	"title": "Analyzing codeswitching rules for english-hindi code-mixed text",
	"authors": [
	{
	"first": "Sushnat",
	"middle": [],
	"last": "Sainik Kumar Mahata",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Makhija",
	"suffix": ""
	}
	],
	"year": 2020,
	"venue": "Emerging Technology in Modelling and Graphics",
	"volume": "",
	"issue": "",
	"pages": "137--145",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Sainik Kumar Mahata, Sushnat Makhija, Ayushi Ag- nihotri, and Dipankar Das. 2020. Analyzing code- switching rules for english-hindi code-mixed text. In Emerging Technology in Modelling and Graphics, pages 137-145, Singapore. Springer Singapore.",
	"links": null
	},
	"BIBREF33": {
	"ref_id": "b33",
	"title": "Code-mixed to monolingual translation framework",
	"authors": [
	{
	"first": "Soumil",
	"middle": [],
	"last": "Sainik Kumar Mahata",
	"suffix": ""
	},
	{
	"first": "Dipankar",
	"middle": [],
	"last": "Mandal",
	"suffix": ""
	},
	{
	"first": "Sivaji",
	"middle": [],
	"last": "Das",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Bandyopadhyay",
	"suffix": ""
	}
	],
	"year": 2019,
	"venue": "Proceedings of the 11th Forum for Information Retrieval Evaluation, FIRE '19",
	"volume": "",
	"issue": "",
	"pages": "30--35",
	"other_ids": {
	"DOI": [
	"10.1145/3368567.3368579"
	]
	},
	"num": null,
	"urls": [],
	"raw_text": "Sainik Kumar Mahata, Soumil Mandal, Dipankar Das, and Sivaji Bandyopadhyay. 2019. Code-mixed to monolingual translation framework. In Proceedings of the 11th Forum for Information Retrieval Evalu- ation, FIRE '19, page 30-35, New York, NY, USA. Association for Computing Machinery.",
	"links": null
	},
	"BIBREF34": {
	"ref_id": "b34",
	"title": "Language identification of bengali-english code-mixed data using character & phonetic based lstm models",
	"authors": [
	{
	"first": "Soumil",
	"middle": [],
	"last": "Mandal",
	"suffix": ""
	},
	{
	"first": "Dipankar",
	"middle": [],
	"last": "Sourya Dipta Das",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Das",
	"suffix": ""
	}
	],
	"year": 2018,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {
	"arXiv": [
	"arXiv:1803.03859"
	]
	},
	"num": null,
	"urls": [],
	"raw_text": "Soumil Mandal, Sourya Dipta Das, and Dipankar Das. 2018. Language identification of bengali-english code-mixed data using character & phonetic based lstm models. arXiv preprint arXiv:1803.03859.",
	"links": null
	},
	"BIBREF35": {
	"ref_id": "b35",
	"title": "Overview of the HASOC Track at FIRE 2020: Hate Speech and Offensive Language Identification in Tamil",
	"authors": [
	{
	"first": "Thomas",
	"middle": [],
	"last": "Mandl",
	"suffix": ""
	},
	{
	"first": "Sandip",
	"middle": [],
	"last": "Modha",
	"suffix": ""
	},
	{
	"first": "Anand",
	"middle": [],
	"last": "Kumar",
	"suffix": ""
	},
	{
	"first": "M",
	"middle": [],
	"last": "",
	"suffix": ""
	},
	{
	"first": "Bharathi Raja Chakravarthi ;",
	"middle": [],
	"last": "Malayalam",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Hindi",
	"suffix": ""
	},
	{
	"first": "German",
	"middle": [],
	"last": "English",
	"suffix": ""
	}
	],
	"year": 2020,
	"venue": "Forum for Information Retrieval Evaluation",
	"volume": "2020",
	"issue": "",
	"pages": "29--32",
	"other_ids": {
	"DOI": [
	"10.1145/3441501.3441517"
	]
	},
	"num": null,
	"urls": [],
	"raw_text": "Thomas Mandl, Sandip Modha, Anand Kumar M, and Bharathi Raja Chakravarthi. 2020. Overview of the HASOC Track at FIRE 2020: Hate Speech and Offensive Language Identification in Tamil, Malay- alam, Hindi, English and German. In Forum for Information Retrieval Evaluation, FIRE 2020, page 29-32, New York, NY, USA. Association for Com- puting Machinery.",
	"links": null
	},
	"BIBREF36": {
	"ref_id": "b36",
	"title": "Spanish-english codeswitching in email communication",
	"authors": [
	{
	"first": "Rosalyn",
	"middle": [],
	"last": "Negr\u00f3n",
	"suffix": ""
	},
	{
	"first": "Goldbarg",
	"middle": [],
	"last": "",
	"suffix": ""
	}
	],
	"year": 2009,
	"venue": "Language@ internet",
	"volume": "6",
	"issue": "3",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Rosalyn Negr\u00f3n Goldbarg. 2009. Spanish-english codeswitching in email communication. Lan- guage@ internet, 6(3).",
	"links": null
	},
	"BIBREF37": {
	"ref_id": "b37",
	"title": "Named Entity Recognition for Code-Mixed Indian Corpus using Meta Embedding",
	"authors": [
	{
	"first": "Ruba",
	"middle": [],
	"last": "Priyadharshini",
	"suffix": ""
	},
	{
	"first": "Mani",
	"middle": [],
	"last": "Bharathi Raja Chakravarthi",
	"suffix": ""
	},
	{
	"first": "John",
	"middle": [
	"P"
	],
	"last": "Vegupatti",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Mccrae",
	"suffix": ""
	}
	],
	"year": 2020,
	"venue": "2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS)",
	"volume": "",
	"issue": "",
	"pages": "68--72",
	"other_ids": {
	"DOI": [
	"10.1109/ICACCS48705.2020.9074379"
	]
	},
	"num": null,
	"urls": [],
	"raw_text": "Ruba Priyadharshini, Bharathi Raja Chakravarthi, Mani Vegupatti, and John P. McCrae. 2020. Named Entity Recognition for Code-Mixed Indian Corpus using Meta Embedding. In 2020 6th International Conference on Advanced Computing and Communi- cation Systems (ICACCS), pages 68-72.",
	"links": null
	},
	"BIBREF38": {
	"ref_id": "b38",
	"title": "IIITT@LT-EDI-EACL2021-Hope Speech Detection: There is always hope in Transformers",
	"authors": [
	{
	"first": "Karthik",
	"middle": [],
	"last": "Puranik",
	"suffix": ""
	},
	{
	"first": "Adeep",
	"middle": [],
	"last": "Hande",
	"suffix": ""
	},
	{
	"first": "Ruba",
	"middle": [],
	"last": "Priyadharshini",
	"suffix": ""
	},
	{
	"first": "Sajeetha",
	"middle": [],
	"last": "Thavareesan",
	"suffix": ""
	},
	{
	"first": "Bharathi Raja",
	"middle": [],
	"last": "Chakravarthi",
	"suffix": ""
	}
	],
	"year": 2021,
	"venue": "Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Karthik Puranik, Adeep Hande, Ruba Priyad- harshini, Sajeetha Thavareesan, and Bharathi Raja Chakravarthi. 2021. IIITT@LT-EDI-EACL2021- Hope Speech Detection: There is always hope in Transformers. In Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion. Association for Computational Linguistics.",
	"links": null
	},
	"BIBREF39": {
	"ref_id": "b39",
	"title": "Yara parser: A fast and accurate dependency parser",
	"authors": [
	{
	"first": "Mohammad",
	"middle": [],
	"last": "Sadegh Rasooli",
	"suffix": ""
	},
	{
	"first": "Joel",
	"middle": [
	"R"
	],
	"last": "Tetreault",
	"suffix": ""
	}
	],
	"year": 2015,
	"venue": "Computing Research Repository",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {
	"arXiv": [
	"arXiv:1503.06733"
	]
	},
	"num": null,
	"urls": [],
	"raw_text": "Mohammad Sadegh Rasooli and Joel R. Tetreault. 2015. Yara parser: A fast and accurate depen- dency parser. Computing Research Repository, arXiv:1503.06733. Version 2.",
	"links": null
	},
	"BIBREF40": {
	"ref_id": "b40",
	"title": "Sentiment analysis for code-mixed indian social media text with distributed representation",
	"authors": [
	{
	"first": "K",
	"middle": [],
	"last": "Shalini",
	"suffix": ""
	},
	{
	"first": "H",
	"middle": [
	"B"
	],
	"last": "Ganesh",
	"suffix": ""
	},
	{
	"first": "M",
	"middle": [
	"A"
	],
	"last": "Kumar",
	"suffix": ""
	},
	{
	"first": "K",
	"middle": [
	"P"
	],
	"last": "Soman",
	"suffix": ""
	}
	],
	"year": 2018,
	"venue": "2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI)",
	"volume": "",
	"issue": "",
	"pages": "1126--1131",
	"other_ids": {
	"DOI": [
	"10.1109/ICACCI.2018.8554835"
	]
	},
	"num": null,
	"urls": [],
	"raw_text": "K. Shalini, H. B. Ganesh, M. A. Kumar, and K. P. Soman. 2018. Sentiment analysis for code-mixed indian social media text with distributed represen- tation. In 2018 International Conference on Ad- vances in Computing, Communications and Infor- matics (ICACCI), pages 1126-1131.",
	"links": null
	},
	"BIBREF41": {
	"ref_id": "b41",
	"title": "Preparing bengali-english code-mixed corpus for sentiment analysis of indian languages",
	"authors": [
	{
	"first": "Sainik",
	"middle": [],
	"last": "Kumar Mahata Soumil Mandal",
	"suffix": ""
	},
	{
	"first": "Dipankar",
	"middle": [],
	"last": "Das",
	"suffix": ""
	}
	],
	"year": 2018,
	"venue": "Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Sainik Kumar Mahata Soumil Mandal and Dipankar Das. 2018. Preparing bengali-english code-mixed corpus for sentiment analysis of indian languages. In Proceedings of the Eleventh International Confer- ence on Language Resources and Evaluation (LREC 2018), Paris, France. European Language Resources Association (ELRA).",
	"links": null
	},
	"BIBREF42": {
	"ref_id": "b42",
	"title": "Findings of the shared task on Troll Meme Classification in Tamil",
	"authors": [
	{
	"first": "Shardul",
	"middle": [],
	"last": "Suryawanshi",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Bharathi Raja Chakravarthi",
	"suffix": ""
	}
	],
	"year": 2021,
	"venue": "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages. Association for Computational Linguistics",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Shardul Suryawanshi and Bharathi Raja Chakravarthi. 2021. Findings of the shared task on Troll Meme Classification in Tamil. In Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages. Association for Compu- tational Linguistics.",
	"links": null
	},
	"BIBREF43": {
	"ref_id": "b43",
	"title": "Multimodal meme dataset (MultiOFF) for identifying offensive content in image and text",
	"authors": [
	{
	"first": "Shardul",
	"middle": [],
	"last": "Suryawanshi",
	"suffix": ""
	},
	{
	"first": "Mihael",
	"middle": [],
	"last": "Bharathi Raja Chakravarthi",
	"suffix": ""
	},
	{
	"first": "Paul",
	"middle": [],
	"last": "Arcan",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Buitelaar",
	"suffix": ""
	}
	],
	"year": 2020,
	"venue": "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
	"volume": "",
	"issue": "",
	"pages": "32--41",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Shardul Suryawanshi, Bharathi Raja Chakravarthi, Mi- hael Arcan, and Paul Buitelaar. 2020. Multimodal meme dataset (MultiOFF) for identifying offensive content in image and text. In Proceedings of the Sec- ond Workshop on Trolling, Aggression and Cyber- bullying, pages 32-41, Marseille, France. European Language Resources Association (ELRA).",
	"links": null
	},
	"BIBREF44": {
	"ref_id": "b44",
	"title": "Sentiment Analysis in Tamil Texts: A Study on Machine Learning Techniques and Feature Representation",
	"authors": [
	{
	"first": "Sajeetha",
	"middle": [],
	"last": "Thavareesan",
	"suffix": ""
	},
	{
	"first": "Sinnathamby",
	"middle": [],
	"last": "Mahesan",
	"suffix": ""
	}
	],
	"year": 2019,
	"venue": "2019 14th Conference on Industrial and Information Systems (ICIIS)",
	"volume": "",
	"issue": "",
	"pages": "320--325",
	"other_ids": {
	"DOI": [
	"10.1109/ICIIS47346.2019.9063341"
	]
	},
	"num": null,
	"urls": [],
	"raw_text": "Sajeetha Thavareesan and Sinnathamby Mahesan. 2019. Sentiment Analysis in Tamil Texts: A Study on Machine Learning Techniques and Feature Rep- resentation. In 2019 14th Conference on Industrial and Information Systems (ICIIS), pages 320-325.",
	"links": null
	},
	"BIBREF45": {
	"ref_id": "b45",
	"title": "Sentiment Lexicon Expansion using Word2vec and fastText for Sentiment Prediction in Tamil texts",
	"authors": [
	{
	"first": "Sajeetha",
	"middle": [],
	"last": "Thavareesan",
	"suffix": ""
	},
	{
	"first": "Sinnathamby",
	"middle": [],
	"last": "Mahesan",
	"suffix": ""
	}
	],
	"year": 2020,
	"venue": "2020 Moratuwa Engineering Research Conference (MERCon)",
	"volume": "",
	"issue": "",
	"pages": "272--276",
	"other_ids": {
	"DOI": [
	"10.1109/MERCon50084.2020.9185369"
	]
	},
	"num": null,
	"urls": [],
	"raw_text": "Sajeetha Thavareesan and Sinnathamby Mahesan. 2020a. Sentiment Lexicon Expansion using Word2vec and fastText for Sentiment Prediction in Tamil texts. In 2020 Moratuwa Engineering Re- search Conference (MERCon), pages 272-276.",
	"links": null
	},
	"BIBREF46": {
	"ref_id": "b46",
	"title": "Word embedding-based Part of Speech tagging in Tamil texts",
	"authors": [
	{
	"first": "Sajeetha",
	"middle": [],
	"last": "Thavareesan",
	"suffix": ""
	},
	{
	"first": "Sinnathamby",
	"middle": [],
	"last": "Mahesan",
	"suffix": ""
	}
	],
	"year": 2020,
	"venue": "2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)",
	"volume": "",
	"issue": "",
	"pages": "478--482",
	"other_ids": {
	"DOI": [
	"10.1109/ICIIS51140.2020.9342640"
	]
	},
	"num": null,
	"urls": [],
	"raw_text": "Sajeetha Thavareesan and Sinnathamby Mahesan. 2020b. Word embedding-based Part of Speech tag- ging in Tamil texts. In 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS), pages 478-482.",
	"links": null
	},
	"BIBREF47": {
	"ref_id": "b47",
	"title": "Finding romanized arabic dialect in code-mixed tweets",
	"authors": [
	{
	"first": "R",
	"middle": [],
	"last": "Clare",
	"suffix": ""
	},
	{
	"first": "Stephen",
	"middle": [],
	"last": "Voss",
	"suffix": ""
	},
	{
	"first": "Jamal",
	"middle": [],
	"last": "Tratz",
	"suffix": ""
	},
	{
	"first": "Douglas",
	"middle": [
	"M"
	],
	"last": "Laoudi",
	"suffix": ""
	},
	{
	"first": "",
	"middle": [],
	"last": "Briesch",
	"suffix": ""
	}
	],
	"year": 2014,
	"venue": "LREC",
	"volume": "",
	"issue": "",
	"pages": "2249--2253",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Clare R Voss, Stephen Tratz, Jamal Laoudi, and Dou- glas M Briesch. 2014. Finding romanized arabic di- alect in code-mixed tweets. In LREC, pages 2249- 2253.",
	"links": null
	},
	"BIBREF48": {
	"ref_id": "b48",
	"title": "Pos tagging of english-hindi code-mixed social media content",
	"authors": [
	{
	"first": "Yogarshi",
	"middle": [],
	"last": "Vyas",
	"suffix": ""
	},
	{
	"first": "Spandana",
	"middle": [],
	"last": "Gella",
	"suffix": ""
	},
	{
	"first": "Jatin",
	"middle": [],
	"last": "Sharma",
	"suffix": ""
	},
	{
	"first": "Kalika",
	"middle": [],
	"last": "Bali",
	"suffix": ""
	},
	{
	"first": "Monojit",
	"middle": [],
	"last": "Choudhury",
	"suffix": ""
	}
	],
	"year": 2014,
	"venue": "Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
	"volume": "",
	"issue": "",
	"pages": "974--979",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Yogarshi Vyas, Spandana Gella, Jatin Sharma, Kalika Bali, and Monojit Choudhury. 2014. Pos tagging of english-hindi code-mixed social media content. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 974-979.",
	"links": null
	},
	"BIBREF49": {
	"ref_id": "b49",
	"title": "Sajeetha Thavareesan, and Bharathi Raja Chakravarthi. 2021. IIITT@DravidianLangTech-EACL2021: Transfer Learning for Offensive Language Detection in Dravidian Languages",
	"authors": [
	{
	"first": "Konthala",
	"middle": [],
	"last": "Yasaswini",
	"suffix": ""
	},
	{
	"first": "Karthik",
	"middle": [],
	"last": "Puranik",
	"suffix": ""
	},
	{
	"first": "Adeep",
	"middle": [],
	"last": "Hande",
	"suffix": ""
	},
	{
	"first": "Ruba",
	"middle": [],
	"last": "Priyadharshini",
	"suffix": ""
	}
	],
	"year": null,
	"venue": "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Konthala Yasaswini, Karthik Puranik, Adeep Hande, Ruba Priyadharshini, Sajeetha Thava- reesan, and Bharathi Raja Chakravarthi. 2021. IIITT@DravidianLangTech-EACL2021: Transfer Learning for Offensive Language Detection in Dravidian Languages. In Proceedings of the First Workshop on Speech and Language Technolo- gies for Dravidian Languages. Association for Computational Linguistics.",
	"links": null
	}
	},
	"ref_entries": {
	"FIGREF0": {
	"uris": null,
	"num": null,
	"type_str": "figure",
	"text": "Classification model for language identification."
	},
	"FIGREF1": {
	"uris": null,
	"num": null,
	"type_str": "figure",
	"text": "Code-Mixed Sentiment Analysis model."
	},
	"TABREF1": {
	"num": null,
	"html": null,
	"content": "<table><tr><td>Metrics</td><td>Value</td></tr><tr><td colspan=\"2\">Accuracy 96.89%</td></tr><tr><td>Precision</td><td>0.94</td></tr><tr><td>Recall</td><td>0.96</td></tr><tr><td>F1-Score</td><td>0.95</td></tr></table>",
	"text": ".",
	"type_str": "table"
	},
	"TABREF2": {
	"num": null,
	"html": null,
	"content": "<table><tr><td/><td>Labels</td><td/></tr><tr><td/><td>(Language Tags)</td><td/></tr><tr><td/><td>Dense Layer</td><td/></tr><tr><td>Bi-Directional</td><td/><td/></tr><tr><td>LSTMs</td><td/><td/></tr><tr><td>LSTM</td><td>LSTM</td><td>LSTM</td></tr><tr><td>LSTM</td><td>LSTM</td><td>LSTM LSTM</td></tr><tr><td>LSTM</td><td>LSTM</td><td>LSTM</td></tr><tr><td>LSTM</td><td>LSTM LSTM</td><td>LSTM LSTM</td></tr><tr><td/><td>Embedding Layer</td><td/></tr><tr><td/><td>Individual Words</td><td/></tr><tr><td/><td>(at character level)</td><td/></tr></table>",
	"text": "Accuracy metrics of the Language Identification model.",
	"type_str": "table"
	},
	"TABREF5": {
	"num": null,
	"html": null,
	"content": "<table><tr><td colspan=\"2\">Model Algorithm</td><td>Features</td><td colspan=\"4\">Accuracy Precision Recall F1-Score</td></tr><tr><td>NB</td><td>CV TF-IDF</td><td>Word Word+Ln Tag Word Word+Ln Tag</td><td>65.23% 69.20% 64.96% 69.68%</td><td>0.52 0.55 0.51 0.56</td><td>0.65 0.69 0.65 0.70</td><td>0.58 0.61 0.57 0.62</td></tr><tr><td>LR</td><td>CV TF-IDF</td><td>Word Word+Ln Tag Word Word+Ln Tag</td><td>65.22% 68.65% 66.54% 70.23%</td><td>0.51 0.53 0.53 0.56</td><td>0.65 0.69 0.67 0.70</td><td>0.57 0.60 0.59 0.62</td></tr><tr><td>SVM</td><td>CV TF-</td><td>Word Word+Ln Tag Word</td><td>65.34% 68.88% 65.89%</td><td>0.52 0.53 0.52</td><td>0.65 0.69 0.66</td><td>0.58 0.60 0.58</td></tr><tr><td/><td>IDF</td><td>Word+Ln Tag</td><td>69.44%</td><td>0.53</td><td>0.69</td><td>0.60</td></tr><tr><td>RF</td><td>CV TF-IDF</td><td>Word Word+Ln Tag Word Word+Ln Tag</td><td>65.12% 69.76% 64.27% 69.60%</td><td>0.49 0.54 0.51 0.53</td><td>0.65 0.70 0.64 0.70</td><td>0.56 0.61 0.57 0.60</td></tr></table>",
	"text": "Comparison of accuracy scores of the developed models built using NN architecture.",
	"type_str": "table"
	},
	"TABREF6": {
	"num": null,
	"html": null,
	"content": "<table/>",
	"text": "Comparison of accuracy scores of the developed models built using ML algorithms.",
	"type_str": "table"
	},
	"TABREF7": {
	"num": null,
	"html": null,
	"content": "<table/>",
	"text": "Final evaluation of the model, developed using Bidirectional LSTMs and Language Tag features.",
	"type_str": "table"
	}
	}
	}
	}