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| "paper_id": "2020", | |
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| "title": "LITL at SMM4H: an old-school feature-based classifier for identifying adverse effects in Tweets", | |
| "authors": [ | |
| { | |
| "first": "Ludovic", | |
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| "last": "Tanguy", | |
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| "email": "[email protected]" | |
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| { | |
| "first": "Lydia-Mai", | |
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| "last": "Ho-Dac", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "[email protected]" | |
| }, | |
| { | |
| "first": "C\u00e9cile", | |
| "middle": [], | |
| "last": "Fabre", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "[email protected]" | |
| }, | |
| { | |
| "first": "Roxane", | |
| "middle": [], | |
| "last": "Bois", | |
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| }, | |
| { | |
| "first": "Touati", | |
| "middle": [], | |
| "last": "Mohamed", | |
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| }, | |
| { | |
| "first": "Yacine", | |
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| "last": "Haddad", | |
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| "abstract": "This paper describes our participation to the SMM4H shared task 2. We designed a linear classifier that estimates whether a tweet mentions an adverse effect associated to a medication. Our system addresses English and French, and is based on a number of ad-hoc word lists and features. These cues were mostly obtained through an extensive corpus analysis of the provided training data. Different weighting schemes were tested (manually tuned or based on a logistic regression), the best one achieving a F1 score of 0.31 for English and 0.15 for French.", | |
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| "text": "This paper describes our participation to the SMM4H shared task 2. We designed a linear classifier that estimates whether a tweet mentions an adverse effect associated to a medication. Our system addresses English and French, and is based on a number of ad-hoc word lists and features. These cues were mostly obtained through an extensive corpus analysis of the provided training data. Different weighting schemes were tested (manually tuned or based on a logistic regression), the best one achieving a F1 score of 0.31 for English and 0.15 for French.", | |
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| "text": "This article describes the participation of the students of the LITL master and their teachers to the Social Media Mining for Health (SMM4H) shared task 2 (Klein et al., 2020) . LITL (stands for Linguistique, Informatique, Technologies du Langage, i.e. Linguistics, IT, Language technologies) is a master's program at the University of Toulouse, France that is mainly aimed at linguistics and humanities students.", | |
| "cite_spans": [ | |
| { | |
| "start": 155, | |
| "end": 175, | |
| "text": "(Klein et al., 2020)", | |
| "ref_id": "BIBREF9" | |
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| "section": "Overview", | |
| "sec_num": "1" | |
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| "text": "The shared task is a binary classification of Twitter messages in different languages, indicating whether the message contains a mention of medication adverse effects. Participation to this task was part of the first year students' curriculum. At this stage, their computer skills were still limited to corpus processing and simple programs, so it was decided that the system's architecture would be a traditional linear classifier based on ad-hoc features. This approach was also deemed justified given the heavily biased distribution of data (known to be an issue for most machine learning techniques). However, the students were encouraged to apply and hone their corpus linguistics skills, and to perform some feature engineering. The approach was the following:", | |
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| "text": "1. Observe the training data with corpus analysis tools, in order to identify the main characteristics of the target (i.e. tweets evoking an adverse effect); 2. Build word lists and design simple features for each of these characteristics; 3. Design a program that computes the features' values on the target data and implements a simple weight-based linear classifier; 4. Tune the weights in order to maximize the classifier's performance on the validation data. Due to the necessity to actually observe and understand the training data only the French and English sets were considered, as none of the students was proficient in Russian.", | |
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| "section": "Overview", | |
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| "text": "For observation and actual processing in both languages the tweets were preprocessed as follows:", | |
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| "text": "\u2022 retweet marks (rt @X) were removed;", | |
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| "text": "\u2022 user names (@XXX) were replaced with a generic and POS-wide unambiguous proper name (Sacha);", | |
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| "text": "\u2022 URLs and email addresses were replaced with generic placeholders (<URL/> and <email/>);", | |
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| "text": "\u2022 non-standard spelling was normalised (e.g. removal of exceeding repeated letters baaad \u2192 bad);", | |
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| "text": "\u2022 POS tagging and lemmatizing were performed, using the Talismane toolkit for both target languages (Urieli, 2013) .", | |
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| "text": "Fifteen word lists were compiled for each language, each one targeting a specific aspect of the tweets content. Those word lists contain keywords extracted from the target tweets and non target tweets using the TXM corpus analysis tool (Heiden et al., 2010) . The lists were extended with existing lexical resources such as sentiment lexicons (e.g. the SocialSent lexicon (Abdaoui et al., 2017) and the FEEL -French Expanded Emotion Lexicon (Hamilton et al., 2016)) and biomedical domain language resources (N\u00e9v\u00e9ol et al., 2014) . Table 1 gives an overview of the word lists designed and used as features for English and French Tweet classification. Each word list led to a numeric feature corresponding to the raw frequency of matching lemmas in the tweet. Two different strategies were considered for dealing with multi-word expressions. Runs 1 and 3 count all items as matches even in case they are also part of an item in another list, e.g. skin (body part) and skin rash (symptom). In contrast, run 2 only counts the longer item (e.g. skin rash as a symptom feature).", | |
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| "text": "(Heiden et al., 2010)", | |
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| "text": "(Abdaoui et al., 2017)", | |
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| "start": 507, | |
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| "text": "(N\u00e9v\u00e9ol et al., 2014)", | |
| "ref_id": "BIBREF12" | |
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| "text": "Table 1", | |
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| ], | |
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| "sec_num": "2" | |
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| "text": "Three additional non-lexical features were also used: number of hash tags, number of URLs and number of Twitter user names (i.e. Sacha, cf. supra).", | |
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| "section": "Technical details", | |
| "sec_num": "2" | |
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| "text": "Each feature was assigned a weight proportional to its relative importance in the decision process. For runs 2 and 3 the weights were individually fixed based on the frequency ratios in target (vs non target) tweets in the training data, and then manually adjusted based on the scores obtained on the validation sets. The best weights were found by progressively increasing the weight of each feature independently of each other until the best F1 score is reached. For run 2, we adopted a principle of equality between features. For run 3, some features were considered as more important than others on the basis of manual observations. Run 1 used a standard logistic regression classifier trained on training data. Table 2 shows the results for each run and for each language on the validation and test sets. The first strategy for dealing with multi-word expressions was clearly better. Manual tuning of the feature weights (which was performed before the students were introduced to machine learning techniques) was promising on the validation set (especially regarding precision) but proved to be much less robust in the test set. Further experiments will be performed in order to assess the added value of selected word lists, compared to more straightforward and non-selective bag-of-words methods, and of course more recent NLP techniques based on word embeddings and neural classifiers. ", | |
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| "section": "Technical details", | |
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| { | |
| "text": "This work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details: http:// creativecommons.org/licenses/by/4.0/.", | |
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| "BIBREF0": { | |
| "ref_id": "b0", | |
| "title": "Feel: a French expanded emotion lexicon", | |
| "authors": [ | |
| { | |
| "first": "Amine", | |
| "middle": [], | |
| "last": "Abdaoui", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "J\u00e9r\u00f4me", | |
| "middle": [], | |
| "last": "Az\u00e9", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Sandra", | |
| "middle": [], | |
| "last": "Bringay", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Pascal", | |
| "middle": [], | |
| "last": "Poncelet", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2017, | |
| "venue": "Language Resources and Evaluation", | |
| "volume": "51", | |
| "issue": "3", | |
| "pages": "833--855", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Amine Abdaoui, J\u00e9r\u00f4me Az\u00e9, Sandra Bringay, and Pascal Poncelet. 2017. Feel: a French expanded emotion lexicon. Language Resources and Evaluation, 51(3):833-855.", | |
| "links": null | |
| }, | |
| "BIBREF1": { | |
| "ref_id": "b1", | |
| "title": "Detection and analysis of drug misuses. a study based on social media messages", | |
| "authors": [ | |
| { | |
| "first": "Elise", | |
| "middle": [], | |
| "last": "Bigeard", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Natalia", | |
| "middle": [], | |
| "last": "Grabar", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Frantz", | |
| "middle": [], | |
| "last": "Thiessard", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2018, | |
| "venue": "Frontiers in pharmacology", | |
| "volume": "9", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Elise Bigeard, Natalia Grabar, and Frantz Thiessard. 2018. Detection and analysis of drug misuses. a study based on social media messages. Frontiers in pharmacology, 9:791.", | |
| "links": null | |
| }, | |
| "BIBREF2": { | |
| "ref_id": "b2", | |
| "title": "UZH@SMM4H: System descriptions", | |
| "authors": [ | |
| { | |
| "first": "Tilia", | |
| "middle": [], | |
| "last": "Ellendorff", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Joseph", | |
| "middle": [], | |
| "last": "Cornelius", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Heath", | |
| "middle": [], | |
| "last": "Gordon", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Nicola", | |
| "middle": [], | |
| "last": "Colic", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Fabio", | |
| "middle": [], | |
| "last": "Rinaldi", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2018, | |
| "venue": "Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "56--60", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Tilia Ellendorff, Joseph Cornelius, Heath Gordon, Nicola Colic, and Fabio Rinaldi. 2018. UZH@SMM4H: System descriptions. In Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task, pages 56-60.", | |
| "links": null | |
| }, | |
| "BIBREF3": { | |
| "ref_id": "b3", | |
| "title": "Mining Twitter for adverse drug reaction mentions: a corpus and classification benchmark", | |
| "authors": [ | |
| { | |
| "first": "Rachel", | |
| "middle": [], | |
| "last": "Ginn", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Pranoti", | |
| "middle": [], | |
| "last": "Pimpalkhute", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Azadeh", | |
| "middle": [], | |
| "last": "Nikfarjam", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Apurv", | |
| "middle": [], | |
| "last": "Patki", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "O'", | |
| "middle": [], | |
| "last": "Karen", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Abeed", | |
| "middle": [], | |
| "last": "Connor", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Karen", | |
| "middle": [], | |
| "last": "Sarker", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Graciela", | |
| "middle": [], | |
| "last": "Smith", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Gonzalez", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2014, | |
| "venue": "Proceedings of the fourth workshop on building and evaluating resources for health and biomedical text processing", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "1--8", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Rachel Ginn, Pranoti Pimpalkhute, Azadeh Nikfarjam, Apurv Patki, Karen O'Connor, Abeed Sarker, Karen Smith, and Graciela Gonzalez. 2014. Mining Twitter for adverse drug reaction mentions: a corpus and classification benchmark. In Proceedings of the fourth workshop on building and evaluating resources for health and biomed- ical text processing, pages 1-8.", | |
| "links": null | |
| }, | |
| "BIBREF4": { | |
| "ref_id": "b4", | |
| "title": "Inducing domain-specific sentiment lexicons from unlabeled corpora", | |
| "authors": [ | |
| { | |
| "first": "Kevin", | |
| "middle": [], | |
| "last": "William L Hamilton", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Jure", | |
| "middle": [], | |
| "last": "Clark", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Dan", | |
| "middle": [], | |
| "last": "Leskovec", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Jurafsky", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2016, | |
| "venue": "Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing", | |
| "volume": "2016", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "William L Hamilton, Kevin Clark, Jure Leskovec, and Dan Jurafsky. 2016. Inducing domain-specific sentiment lexicons from unlabeled corpora. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing, volume 2016, page 595.", | |
| "links": null | |
| }, | |
| "BIBREF5": { | |
| "ref_id": "b5", | |
| "title": "TXM : Une plateforme logicielle opensource pour la textom\u00e9trie -conception et d\u00e9veloppement", | |
| "authors": [ | |
| { | |
| "first": "Serge", | |
| "middle": [], | |
| "last": "Heiden", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Jean-Philippe", | |
| "middle": [], | |
| "last": "Magu\u00e9", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "B\u00e9n\u00e9dicte", | |
| "middle": [], | |
| "last": "Pincemin", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2010, | |
| "venue": "10th International Conference on the Statistical Analysis of Textual Data -JADT 2010", | |
| "volume": "2", | |
| "issue": "", | |
| "pages": "1021--1032", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Serge Heiden, Jean-Philippe Magu\u00e9, and B\u00e9n\u00e9dicte Pincemin. 2010. TXM : Une plateforme logicielle open- source pour la textom\u00e9trie -conception et d\u00e9veloppement. In Sergio Bolasco, Isabella Chiari, and Luca Giu- liano, editors, 10th International Conference on the Statistical Analysis of Textual Data -JADT 2010, volume 2, pages 1021-1032, Rome, Italy. Edizioni Universitarie di Lettere Economia Diritto.", | |
| "links": null | |
| }, | |
| "BIBREF6": { | |
| "ref_id": "b6", | |
| "title": "A hybrid approach for drug abuse events extraction from Twitter", | |
| "authors": [ | |
| { | |
| "first": "Ferdaous", | |
| "middle": [], | |
| "last": "Jenhani", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Mohamed", | |
| "middle": [ | |
| "Salah" | |
| ], | |
| "last": "Gouider", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Lamjed", | |
| "middle": [], | |
| "last": "Ben Said", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2016, | |
| "venue": "Procedia computer science", | |
| "volume": "96", | |
| "issue": "", | |
| "pages": "1032--1040", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Ferdaous Jenhani, Mohamed Salah Gouider, and Lamjed Ben Said. 2016. A hybrid approach for drug abuse events extraction from Twitter. Procedia computer science, 96:1032-1040.", | |
| "links": null | |
| }, | |
| "BIBREF7": { | |
| "ref_id": "b7", | |
| "title": "Hybrid system for information extraction from social media text: Drug abuse case study", | |
| "authors": [ | |
| { | |
| "first": "Ferdaous", | |
| "middle": [], | |
| "last": "Jenhani", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Mohamed", | |
| "middle": [ | |
| "Salah" | |
| ], | |
| "last": "Gouider", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Lamjed", | |
| "middle": [], | |
| "last": "Ben Said", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2019, | |
| "venue": "Procedia Computer Science", | |
| "volume": "159", | |
| "issue": "", | |
| "pages": "688--697", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Ferdaous Jenhani, Mohamed Salah Gouider, and Lamjed Ben Said. 2019. Hybrid system for information extrac- tion from social media text: Drug abuse case study. Procedia Computer Science, 159:688-697.", | |
| "links": null | |
| }, | |
| "BIBREF8": { | |
| "ref_id": "b8", | |
| "title": "NRC-Canada at SMM4H shared task: Classifying tweets mentioning adverse drug reactions and medication intake", | |
| "authors": [ | |
| { | |
| "first": "Svetlana", | |
| "middle": [], | |
| "last": "Kiritchenko", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "M", | |
| "middle": [], | |
| "last": "Saif", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Jason", | |
| "middle": [], | |
| "last": "Mohammad", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Berry", | |
| "middle": [], | |
| "last": "Morin", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "De Bruijn", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2017, | |
| "venue": "SMM4H@ AMIA", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Svetlana Kiritchenko, Saif M Mohammad, Jason Morin, and Berry de Bruijn. 2017. NRC-Canada at SMM4H shared task: Classifying tweets mentioning adverse drug reactions and medication intake. In SMM4H@ AMIA.", | |
| "links": null | |
| }, | |
| "BIBREF9": { | |
| "ref_id": "b9", | |
| "title": "Overview of the fifth social media mining for health applications (#smm4h) shared tasks at coling 2020", | |
| "authors": [ | |
| { | |
| "first": "Ari", | |
| "middle": [ | |
| "Z" | |
| ], | |
| "last": "Klein", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ilseyar", | |
| "middle": [], | |
| "last": "Alimova", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ivan", | |
| "middle": [], | |
| "last": "Flores", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Arjun", | |
| "middle": [], | |
| "last": "Magge", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Zulfat", | |
| "middle": [], | |
| "last": "Miftahutdinov", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Anne-Lyse", | |
| "middle": [], | |
| "last": "Minard", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Karen", | |
| "middle": [ | |
| "O" | |
| ], | |
| "last": "Connor", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Abeed", | |
| "middle": [], | |
| "last": "Sarker", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Elena", | |
| "middle": [], | |
| "last": "Tutubalina", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Davy", | |
| "middle": [], | |
| "last": "Weissenbacher", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Graciela", | |
| "middle": [], | |
| "last": "Gonzalez-Hernandez", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2020, | |
| "venue": "Proceedings of the Fifth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Ari Z. Klein, Ilseyar Alimova, Ivan Flores, Arjun Magge, Zulfat Miftahutdinov, Anne-Lyse Minard, Karen O'Connor, Abeed Sarker, Elena Tutubalina, Davy Weissenbacher, and Graciela Gonzalez-Hernandez. 2020. Overview of the fifth social media mining for health applications (#smm4h) shared tasks at coling 2020. In Proceedings of the Fifth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task.", | |
| "links": null | |
| }, | |
| "BIBREF10": { | |
| "ref_id": "b10", | |
| "title": "Separating fact from fear: Tracking flu infections on Twitter", | |
| "authors": [ | |
| { | |
| "first": "Alex", | |
| "middle": [], | |
| "last": "Lamb", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Michael", | |
| "middle": [], | |
| "last": "Paul", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Mark", | |
| "middle": [], | |
| "last": "Dredze", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2013, | |
| "venue": "Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "789--795", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Alex Lamb, Michael Paul, and Mark Dredze. 2013. Separating fact from fear: Tracking flu infections on Twitter. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 789-795.", | |
| "links": null | |
| }, | |
| "BIBREF11": { | |
| "ref_id": "b11", | |
| "title": "Participation de l'IRISA\u00e0 DeFT 2018 : classification et annotation d'opinion dans des tweets", | |
| "authors": [ | |
| { | |
| "first": "Anne-Lyse", | |
| "middle": [], | |
| "last": "Minard", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Christian", | |
| "middle": [], | |
| "last": "Raymond", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Vincent", | |
| "middle": [], | |
| "last": "Claveau", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2018, | |
| "venue": "Actes de la conf\u00e9rence Traitement Automatique de la Langue Naturelle", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Anne-Lyse Minard, Christian Raymond, and Vincent Claveau. 2018. Participation de l'IRISA\u00e0 DeFT 2018 : classification et annotation d'opinion dans des tweets. In Actes de la conf\u00e9rence Traitement Automatique de la Langue Naturelle, TALN 2018, page 265.", | |
| "links": null | |
| }, | |
| "BIBREF12": { | |
| "ref_id": "b12", | |
| "title": "Language resources for French in the biomedical domain", | |
| "authors": [ | |
| { | |
| "first": "Aur\u00e9lie", | |
| "middle": [], | |
| "last": "N\u00e9v\u00e9ol", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Julien", | |
| "middle": [], | |
| "last": "Grosjean", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "St\u00e9fan", | |
| "middle": [ | |
| "Jacques" | |
| ], | |
| "last": "Darmoni", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Pierre", | |
| "middle": [], | |
| "last": "Zweigenbaum", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2014, | |
| "venue": "Proceedings of LREC", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "2146--2151", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Aur\u00e9lie N\u00e9v\u00e9ol, Julien Grosjean, St\u00e9fan Jacques Darmoni, Pierre Zweigenbaum, et al. 2014. Language resources for French in the biomedical domain. In Proceedings of LREC, pages 2146-2151.", | |
| "links": null | |
| }, | |
| "BIBREF13": { | |
| "ref_id": "b13", | |
| "title": "Pharmacovigilance on Twitter? Mining tweets for adverse drug reactions", | |
| "authors": [ | |
| { | |
| "first": "O'", | |
| "middle": [], | |
| "last": "Karen", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Pranoti", | |
| "middle": [], | |
| "last": "Connor", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Azadeh", | |
| "middle": [], | |
| "last": "Pimpalkhute", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Rachel", | |
| "middle": [], | |
| "last": "Nikfarjam", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Karen", | |
| "middle": [ | |
| "L" | |
| ], | |
| "last": "Ginn", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Graciela", | |
| "middle": [], | |
| "last": "Smith", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Gonzalez", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2014, | |
| "venue": "AMIA annual symposium proceedings", | |
| "volume": "2014", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Karen O'Connor, Pranoti Pimpalkhute, Azadeh Nikfarjam, Rachel Ginn, Karen L Smith, and Graciela Gonzalez. 2014. Pharmacovigilance on Twitter? Mining tweets for adverse drug reactions. In AMIA annual symposium proceedings, volume 2014, page 924. American Medical Informatics Association.", | |
| "links": null | |
| }, | |
| "BIBREF14": { | |
| "ref_id": "b14", | |
| "title": "Overview of the second social media mining for health (SMM4) shared tasks at amia", | |
| "authors": [ | |
| { | |
| "first": "Abeed", | |
| "middle": [], | |
| "last": "Sarker", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Graciela", | |
| "middle": [], | |
| "last": "Gonzalez-Hernandez", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2017, | |
| "venue": "Training", | |
| "volume": "", | |
| "issue": "10", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Abeed Sarker and Graciela Gonzalez-Hernandez. 2017. Overview of the second social media mining for health (SMM4) shared tasks at amia 2017. Training, 1(10,822):1239.", | |
| "links": null | |
| }, | |
| "BIBREF15": { | |
| "ref_id": "b15", | |
| "title": "Portable automatic text classification for adverse drug reaction detection via multi-corpus training", | |
| "authors": [ | |
| { | |
| "first": "Abeed", | |
| "middle": [], | |
| "last": "Sarker", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Graciela", | |
| "middle": [], | |
| "last": "Gonzalez", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2015, | |
| "venue": "Journal of biomedical informatics", | |
| "volume": "53", | |
| "issue": "", | |
| "pages": "196--207", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Abeed Sarker and Graciela Gonzalez. 2015. Portable automatic text classification for adverse drug reaction detection via multi-corpus training. Journal of biomedical informatics, 53:196-207.", | |
| "links": null | |
| }, | |
| "BIBREF16": { | |
| "ref_id": "b16", | |
| "title": "Social media mining shared task workshop", | |
| "authors": [ | |
| { | |
| "first": "Abeed", | |
| "middle": [], | |
| "last": "Sarker", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Azadeh", | |
| "middle": [], | |
| "last": "Nikfarjam", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Graciela", | |
| "middle": [], | |
| "last": "Gonzalez", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2016, | |
| "venue": "Biocomputing 2016: Proceedings of the Pacific Symposium", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "581--592", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Abeed Sarker, Azadeh Nikfarjam, and Graciela Gonzalez. 2016. Social media mining shared task workshop. In Biocomputing 2016: Proceedings of the Pacific Symposium, pages 581-592.", | |
| "links": null | |
| }, | |
| "BIBREF17": { | |
| "ref_id": "b17", | |
| "title": "Robust French syntax analysis: reconciling statistical methods and linguistic knowledge in the Talismane toolkit", | |
| "authors": [ | |
| { | |
| "first": "Assaf", | |
| "middle": [], | |
| "last": "Urieli", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2013, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Assaf Urieli. 2013. Robust French syntax analysis: reconciling statistical methods and linguistic knowledge in the Talismane toolkit. Phd thesis, University of Toulouse, France.", | |
| "links": null | |
| }, | |
| "BIBREF18": { | |
| "ref_id": "b18", | |
| "title": "Overview of the third social media mining for health (SMM4H) shared tasks at EMNLP", | |
| "authors": [ | |
| { | |
| "first": "Davy", | |
| "middle": [], | |
| "last": "Weissenbacher", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Abeed", | |
| "middle": [], | |
| "last": "Sarker", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Michael", | |
| "middle": [], | |
| "last": "Paul", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Graciela", | |
| "middle": [], | |
| "last": "Gonzalez", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2018, | |
| "venue": "Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "13--16", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Davy Weissenbacher, Abeed Sarker, Michael Paul, and Graciela Gonzalez. 2018. Overview of the third social media mining for health (SMM4H) shared tasks at EMNLP 2018. In Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task, pages 13-16.", | |
| "links": null | |
| }, | |
| "BIBREF19": { | |
| "ref_id": "b19", | |
| "title": "Overview of the fourth social media mining for health (SMM4H) shared tasks at ACL 2019", | |
| "authors": [ | |
| { | |
| "first": "Davy", | |
| "middle": [], | |
| "last": "Weissenbacher", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Abeed", | |
| "middle": [], | |
| "last": "Sarker", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Arjun", | |
| "middle": [], | |
| "last": "Magge", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ashlynn", | |
| "middle": [], | |
| "last": "Daughton", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "O'", | |
| "middle": [], | |
| "last": "Karen", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Michael", | |
| "middle": [], | |
| "last": "Connor", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Graciela", | |
| "middle": [], | |
| "last": "Paul", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Gonzalez", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2019, | |
| "venue": "Proceedings of the Fourth Social Media Mining for Health Applications (# SMM4H) Workshop & Shared Task", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "21--30", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Davy Weissenbacher, Abeed Sarker, Arjun Magge, Ashlynn Daughton, Karen O'Connor, Michael Paul, and Gra- ciela Gonzalez. 2019. Overview of the fourth social media mining for health (SMM4H) shared tasks at ACL 2019. In Proceedings of the Fourth Social Media Mining for Health Applications (# SMM4H) Workshop & Shared Task, pages 21-30.", | |
| "links": null | |
| }, | |
| "BIBREF20": { | |
| "ref_id": "b20", | |
| "title": "Recognizing contextual polarity in phrase-level sentiment analysis", | |
| "authors": [ | |
| { | |
| "first": "Theresa", | |
| "middle": [], | |
| "last": "Wilson", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Janyce", | |
| "middle": [], | |
| "last": "Wiebe", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Paul", | |
| "middle": [], | |
| "last": "Hoffmann", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2005, | |
| "venue": "Proceedings of human language technology conference and conference on empirical methods in natural language processing", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "347--354", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2005. Recognizing contextual polarity in phrase-level senti- ment analysis. In Proceedings of human language technology conference and conference on empirical methods in natural language processing, pages 347-354.", | |
| "links": null | |
| } | |
| }, | |
| "ref_entries": { | |
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