| { | |
| "paper_id": "C12-1000", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T13:23:51.216223Z" | |
| }, | |
| "title": "Program Committee", | |
| "authors": [ | |
| { | |
| "first": "Martin", | |
| "middle": [], | |
| "last": "Kay", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "Christian", | |
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| "last": "Boitet", | |
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| { | |
| "first": "Pushpak", | |
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| "last": "Bhattacharyya", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Rajeev", | |
| "middle": [], | |
| "last": "Sangal", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Laurent Besacier", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "", | |
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| "last": "Sadao Kuroshashi", | |
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| "email": "" | |
| }, | |
| { | |
| "first": "Rajeev", | |
| "middle": [], | |
| "last": "Sangal Vasant", | |
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| "email": "" | |
| }, | |
| { | |
| "first": "Zende", | |
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| "last": "Rupash", | |
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| { | |
| "first": "Modak", | |
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| "last": "Ganesh", | |
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| { | |
| "first": "Ramakrishnan", | |
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| "last": "Balamurali", | |
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| { | |
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| "N" | |
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| { | |
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| { | |
| "first": "Liqiang", | |
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| { | |
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| { | |
| "first": "Xiangyu", | |
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| { | |
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| { | |
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| { | |
| "first": "Hoang-Quynh", | |
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| { | |
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| "abstract": "The members of the International Committee on Computational Linguistics were immensely privileged to be able to accept the invitation to hold our 24th COLING Conference here in India, a country which can justly be thought of as the center of the linguistic world. This is where Panini wrote the first formal grammar in the 6th century BC and where a linguistic diversity flourishes today that is nothing short of astounding to the rest of us. This conference has received twice as many submissions as any of its predecessors and, in many ways, is twice as rich because of the high proportion of contributions by teachers, researchers and, above all, students. Many are from India and other countries, such as Iran, with long and diverse linguistic traditions. There are challenges here for linguists of all varieties, most especially for those who put their faith in n-grams and machine learning. The 195 full-length technical papers in 5 parallel tracks, 138 posters, and 66 demonstrations that will be presented still constitute no more than a quarter of the total number of submissions. The chairs of some of the 26 program subcommittees were overwhelmed with both their number and their quality. The International Committee is always greatly indebted to the area chairs and reviewers for the invaluable work that they do. Never so much as on this occasion. Our greatest debt is clearly to our colleagues here in Mumbai, as will become clear to all as the week proceeds. They were even less well equipped than we on the permanent committee to predict what they were getting into, but they have risen to the occasion in every way and you will find them to be immensely warm, helpful, and resourceful hosts. COLING's founding fathers wanted these conferences to be more than learned presentations. They wanted them to be opportunities to meet, and talk and delight in the company of other who share our fascination with language and the processes that make it work. Some call this the COLING spirit. There is nowhere that could nurture this spirit more effectively than here in India.", | |
| "pdf_parse": { | |
| "paper_id": "C12-1000", | |
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| "abstract": [ | |
| { | |
| "text": "The members of the International Committee on Computational Linguistics were immensely privileged to be able to accept the invitation to hold our 24th COLING Conference here in India, a country which can justly be thought of as the center of the linguistic world. This is where Panini wrote the first formal grammar in the 6th century BC and where a linguistic diversity flourishes today that is nothing short of astounding to the rest of us. This conference has received twice as many submissions as any of its predecessors and, in many ways, is twice as rich because of the high proportion of contributions by teachers, researchers and, above all, students. Many are from India and other countries, such as Iran, with long and diverse linguistic traditions. There are challenges here for linguists of all varieties, most especially for those who put their faith in n-grams and machine learning. The 195 full-length technical papers in 5 parallel tracks, 138 posters, and 66 demonstrations that will be presented still constitute no more than a quarter of the total number of submissions. The chairs of some of the 26 program subcommittees were overwhelmed with both their number and their quality. The International Committee is always greatly indebted to the area chairs and reviewers for the invaluable work that they do. Never so much as on this occasion. Our greatest debt is clearly to our colleagues here in Mumbai, as will become clear to all as the week proceeds. They were even less well equipped than we on the permanent committee to predict what they were getting into, but they have risen to the occasion in every way and you will find them to be immensely warm, helpful, and resourceful hosts. COLING's founding fathers wanted these conferences to be more than learned presentations. They wanted them to be opportunities to meet, and talk and delight in the company of other who share our fascination with language and the processes that make it work. Some call this the COLING spirit. There is nowhere that could nurture this spirit more effectively than here in India.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Abstract", | |
| "sec_num": null | |
| } | |
| ], | |
| "body_text": [ | |
| { | |
| "text": "It is a matter of great pride that the 24 th International Conference on Computational Linguistics (COLING 2012) is taking place in India, the land of multilinguality and multiculure. The organization of an event of COLING's scale takes enormous energy, planning and time. Two years back, in Beijing, when COLING was awarded to India, we knew that the task will be demanding, and happily for us, the NLP team at IIT Bombay, the organzing institute, has risen to the occasion.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction from the Organizing Chairs", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "At the time of going to press, the total number of registrants in COLING has exceeded 700. With delegates coming from 60 countries, COLING 2012 will witness a colourful diversity of language and culture, and fittingly so. Conforming to current practices of international conferences, there are two days of workshops and tutorials before the main conference and one day of workshop immediately after. 15 focussed and topical workshops will be attended by about 300 delegates, as will be 6 high quality tutorials of contemporary interest.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction from the Organizing Chairs", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Social events include a reception in the evening of 10 th Dec, a banquet in a nearby 7 star hotel on 11 th Dec, an excursion to the famed Bhaja caves on 12 th Dec and a cultural evening of Indian classical music on 13 th Dec. Bhaja Caves, built in the period 3 r d century BC to 2 nd century CE, is a set of Buddhist monastery-caves near the hill station of Lonavala, nestled in the Sahayadri mountain ranges, about 90 km to the south-east of Mumbai. There will be cultural evening on the fourth day of the conference, featuring a solo performance on \"tabla\", the representative of Indian percussion instruments, and another solo on Sitar, an instrument that drew world's attention Indian classical music tradition.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction from the Organizing Chairs", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Indian Institute of Technology Bombay is fittingly the host of COLING 2012. IITs have, over the years, emerged as the premier institutes of technology in India. The Computer Science and Engineering Department at IIT Bombay is one of the largest and oldest Departments of CSE in the country. Each and every member of the 40 strong NLP group at IIT Bombay is toiling hard to make COLING 2012 a resounding success.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction from the Organizing Chairs", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The Government and industries have been our generous sponsors. All their names and logos are to be found in printed and USB proceedings. We thank them wholeheartedly.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction from the Organizing Chairs", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Technology Development in Indian Languages (TDIL) project of Department of IT, Ministry of Communication and Information Technology, has been the harbinger of growth of NLP in India. COLING happening in India is a result of this long history of active patronage. Logistics wise, the \"large events\" -inauguration, invited speeches, reception and the cultural program -are in the convocation hall of IIT Bombay. Oral presentations are all in the newly constructed Victor Menezes Convention Center (VMCC) about 200 mtrs from the convocation hall. Poster presentations are in the convocation hall, except on the first day, when it is VMCC.", | |
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| "section": "Introduction from the Organizing Chairs", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "A very competent team of volunteers will be available for any assistance. We hope COLING participants will have a memorable time in India. be taken as a challenge to the widespread assumption that learners are innately equipped with a format for grammatical description. For if a generative grammar can be arrived at purely by minimizing description length, without relying on any further prior assumptions, might not language acquisition by humans proceed in the same way? I will argue that this is not the case, because there is no effective procedure for constructing the maximally compact representation without prior analytic bias. In fact, the near-perfection of Panini's grammar and its metalanguage required handcrafting by many generations of grammarians. In contrast, the rapidity of normal language acquisition, and the existence of robust cross-linguistic generalizations, remains a persuasive argument for UG.", | |
| "cite_spans": [], | |
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| "eq_spans": [], | |
| "section": "Introduction from the Organizing Chairs", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "information. The interpreter is constantly establishing logical connections on-line between what has already been comprehended and what is being heard, relying on multiple associations that have been formed while preparing for a specific assignment. The interpreter's ability to link new to existing information is thus one of the prime skills to be developed during skill acquisition: being able to associate multiple facets of data in neural networks with only a single sensory trigger firing up the entire network of associated facts emerges as one of the most important factors for successful skill acquisition. Fluency and speeded expert performance develop in a learning environment that is highly contextualized and provides multiple exposures to information so that the task can be executed efficiently as the multilingual brain adapts to complex demands.", | |
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| "eq_spans": [], | |
| "section": "Introduction from the Organizing Chairs", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Our understanding of skill acquisition in interpreting then informs our pedagogical approach and allows us to design learning environments for even the most extreme contexts, with socio-cultural environments characterized by political instability and conflict, where skills need to be acquired swiftly and reliably.", | |
| "cite_spans": [], | |
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| "section": "Introduction from the Organizing Chairs", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "xlii", | |
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| "section": "Introduction from the Organizing Chairs", | |
| "sec_num": null | |
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| { | |
| "text": "Professor Emeritus, Kyoto University Former President, National Diet (Congress) Library, Japan", | |
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| "section": "Digital Book, Digital Library, and Natural Language Processing Prof. Makoto Nagao", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The following topics will be discussed with the author's experience with natural language processing and its applications to digital library.", | |
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| "section": "Abstract", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "1. Features of forth-coming digital books compared to the present-day paper books 2. Features of digital library which organizes digital books and offers highly sophisticated utilization of knowledge accumulated in digital library. This includes problems in digitization, structuring of a book according to the table of contents, varieties of retrieval methods which extract sections of a book, linking related parts of books as a hypertext structure, etc.", | |
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| "section": "Abstract", | |
| "sec_num": null | |
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| { | |
| "text": "3. Construction of an ideal digital library based on these features 4. Natural language processing technologies which are required for the construction of future digital libraries.", | |
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| "section": "Abstract", | |
| "sec_num": null | |
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| { | |
| "text": "xliii", | |
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| "section": "Abstract", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Prof. Dipti Misra Sharma", | |
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| "section": "NLP from Paninian Perspective Prof. Dipti Misra Sharma", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Akshar Bharati, for several years now, has been pursuing NLP basing its linguistic models on Paninian grammatical framework. The talk will re-look at how the concepts from Panini's Grammar help in selecting and modelling linguistically informed NLP (both building resources and systems). (Akshar Bharati et al, 1995) proposed Computational Paninian Grammar models for various levels of linguistic analysis. It is noticed that it works well for relatively free word order languages. Not only, the insights from Panini's grammar help in exploiting morphological properties in computationally efficient parsing but also help in the selection of appropriate features for better machine learning.", | |
| "cite_spans": [ | |
| { | |
| "start": 288, | |
| "end": 316, | |
| "text": "(Akshar Bharati et al, 1995)", | |
| "ref_id": null | |
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| "section": "Abstract", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Panini's grammar focusses on how language is used for comunication. Thus, language is viewed as a system which encodes information. There are three major schools of thought in the Indian grammatical tradition, the grammarians, the logicians and the text analysts. All of these schools lay emphasis on interpretation of meaning (Shaabdabodha)from what is given in a sentence. The grammar provides ways of identification of various linguistic units, their generation processes, relations across units and the syntactic realization of these relations. The talk will look at how a principled application of the concepts and the methods given in this tradition help in developing efficient computational models.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Abstract", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Most parsing approaches in NLP adopt either a constituency based grammar model or a dependency based one. Conversion from one to the other, combining constituency and dependency representation and producing a hyprid tree are some of the areas that the scholars in NLP have been looking at in the direction of bringing the two approaches together. However, Paninian approach suggests that languages encode information both ways. Thus, the talk will also explore whether both consituency and dependency can be incorporated in a single model and whether this would lead to better parsing.", | |
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| "section": "Abstract", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "xliv COLING 2012", | |
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| "section": "Abstract", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "24th International Conference on Computational Linguistics", | |
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| "section": "Abstract", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "xlv", | |
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| "section": "Technical Papers", | |
| "sec_num": null | |
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| ], | |
| "back_matter": [ | |
| { | |
| "text": "Naoki Hirayama, Shinsuke Mori and Hiroshi G. ", | |
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| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Statistical Method of Building Dialect Language Models for ASR Systems", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Panini attempted, and to a considerable extent succeeded, in constructing the shortest possible complete grammar of Sanskrit that contains a description of its own metalanguage. Minimizing the total length of the grammar required introducing a rule or convention just in case it achieves overall economies in the grammar which outweigh the cost of stating it.The grammar presupposes nothing beyond certain elementary relations (such as \"before\" and \"after\") and operations (such as \"replace\"). Based on them, it defines a rich descriptive formalism. Simplicity dictates the inclusion of rules of grammar that encode all generalizations about Sanskrit phonology, morphology, and syntax, as well as of rules that define its grammatical categories, and of metarules that stipulate how rules of grammar apply and interact with each other. The grammar uses a fixed rule format, phonological and morphosyntactic features, rule ordering, cyclicity, blocking, the equivalent of Theta roles, inheritance hierarchies, and several hundred technical terms denoting classes of lexemes and morphemes. Four levels of representation (approximately corresponding to semantics, syntax, morphology, and phonology) emerge from the analysis.Completeness of empirical coverage requires, among other things, the exhaustive treatment of derivational morphology, clausal syntax, variation (three degrees of optionality), and even of certain dialectal and sociolinguistic facts. Some of the abovementioned devices could be dispensed with in a less exhaustive description, as I will illustrate with the karaka system.The grammar appears to be very nearly optimal. Although this has not be proved (except for certain subsystems), it appears likely, for no-one has been able to shorten the grammar in non-trivial ways (without losing content), either by modifying the rules without changing the metalanguage, or by modifying the metalanguage with additional devices and conventions, or removing some of the existing ones.It would be anachronistic to construe the formal apparatus used by Panini as embodying a \"theory\": from his perspective it merely serves to compress the grammar. But the fact that many of the same conventions and principles that modern generative grammar posits as universals of language emerge just from the attempt to construct the maximally compact description of a single language is quite remarkable. It could ", | |
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| "section": "Abstract", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Real-time human communication across language barriers relies on consecutive and simultaneous interpretation, a complex cognitive skill that can be acquired only over a certain period of time. Interpreting novices differ from interpreting experts in terms of their knowledge and knowledge organization, their analytical strategies, their use of memory processes, and the smoothness and speed with which they execute the interpreting task. In order to be able to move from comprehending a speech in one language and simultaneously interpreting that speech into another, the learner needs to make considerable adaptations to component processes of tasks already mastered, for the most part, before even being admitted to an interpreter training program. These adaptations concern mostly language comprehension and knowledge organization, component skills non-interpreters need in order to communicate. One must thus assume that significant changes occur in brain activity (functional changes or plasticity) and brain structure (structural plasticity) during the acquisition of interpreting skills that are the result of learning, knowledge re-organization, strategy acquisition, and task monitoring.In investigating this hypothesis we have recently found evidence for brain structural plasticity in individuals training to become simultaneous interpreters as they develop expertise in this skill. We found that in interpreting students, but not in matched multilingual controls, there is an increase in gray matter volume over the course of a 15-month training program in brain regions known to be involved not only in semantic processing but also in aspects of executive function and error monitoring. Tasks involving the conversion of content from one language to another (i.e., translation and interpretation) mainly engage a left-lateralized cortico-subcortical circuit, including the basal ganglia, inferior frontal gyrus, and DLPFC. There is strong anatomical support for functional links between these regions. We propose that the evidence suggests the presence of two distinct networks contributing to the executive control of language. Although perturbing either may have superficially similar behavioral consequences, they are likely to have differing roles.The plasticity of the brain allows for reshaping and reorganization, acquiring expertise in a task involves the generation of new neuronal connections whose survival is dependent on stimulation through extended electrical pulses that reverberate in the neural net to establish associations and connections between areas of specialized", | |
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| "raw_text": "Shujian Huang, Nanjing University, China. Jimmy Huang, York Univ., Canada. Chu-Ren Huang, Affiliation unknown, China. Matthias Huck, RWTH Aachen University, Germany. Samar Husain, University of Potsdam, Germany. Sarmad Hussain, KICS, Pakistan. Nancy Ide, Vassar College, United States.", | |
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| "content": "<table><tr><td>Multi-Dimensional Feature Merger for Question Answering Apoorv Agarwal, J William Murdock, Jennifer Chu-Carroll, Adam Lally and Aditya Kalyanpur 1</td></tr><tr><td>Unsupervised Discovery of Relations and Discriminative Extraction Patterns Alan Akbik, Larysa Visengeriyeva, Priska Herger, Holmer Hemsen and Alexander L\u00f6ser 17</td></tr><tr><td>Automatic Detection of Point of View Differences in Wikipedia Khalid Al Khatib, Experiments Prague Dependency Treebank 2.5 -a Revisited Version of PDT 2.0 Natural Language Generation for Nature Conservation: Automating Feedback to Help Volunteers Identify Bumblebee Species Steven Blake, Advaith Siddharthan, Hien Nguyen, Nirwan Sharma, Anne-Marie Robinson, Elaine O'Mahony, Can Identifying</td></tr></table>", | |
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| "text": "Hinrich Sch\u00fctze and Cathleen Kantner . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 SpeedRead: A Fast Named Entity Recognition Pipeline Rami Al-Rfou' and Steven Skiena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 Eleftherios Avramidis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Constructing Reference Semantic Predictions from Biomedical Knowledge Sources Demeke Ayele, Jean-Pierre Chevallet, Million Meshesha and Getnet Kassie . . . . . . . . . 133 Translation Quality-Based Supplementary Data Selection by Incremental Update of Translation Models Pratyush Banerjee, Sudip Kumar Naskar, Johann Roturier, Andy Way and Josef van Genabith 149 Text Reuse Detection using a Composition of Text Similarity Measures Daniel B\u00e4r, Torsten Zesch and Iryna Gurevych . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 Deriving Paraphrases for Highly Inflected Languages from Comparable Documents Kfir Bar and Nachum Dershowitz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Harvesting Parallel Text in Multiple Languages with Limited Supervision Luciano Barbosa, Vivek Kumar Rangarajan Sridhar, Mahsa Yarmohammadi and Srinivas Bangalore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 An Evaluation of Statistical Post-Editing Systems Applied to RBMT and SMT Systems Hanna B\u00e9chara, Rapha\u00ebl Rubino, Yifan He, Yanjun Ma and Josef van Genabith . . . . . 215 Eduard Bej\u010dek, Jarmila Panevov\u00e1, Jan Popelka, Pavel Stra\u0148\u00e1k, Magda \u0160ev\u010d\u00edkov\u00e1, Jan \u0160t\u011bp\u00e1nek and Zden\u011bk \u017dabokrtsk\u00fd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 xxv Deriving a Lexicon for a Precision Grammar from Language Documentation Resources: A Case Study of Chintang Emily M. Bender, Robert Schikowski and Balthasar Bickel . . . . . . . . . . . . . . . . . . . . . . . . . . 247 Quantifying Semantics using Complex Network Analysis Chris Biemann, Stefanie Roos and Karsten Weihe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 Improvements to Training an RNN parser Richard Billingsley and James Curran. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 Thread Specific Features are Helpful for Identifying Subjectivity Orientation of Online Forum Threads Prakhar Biyani, Sumit Bhatia, Cornelia Caragea and Prasenjit Mitra . . . . . . . . . . . . . . . . 295 Ben Darvill, Chris Mellish and Rene van der Wal . . . . . . . . . . . . . . . . . . . . 311 Studying the Effect of Input Size for Bayesian Word Segmentation on the Providence Corpus Benjamin B\u00f6rschinger, Katherine Demuth and Mark Johnson . . . . . . . . . . . . . . . . . . . . . . . 325 Bayesian Language Modelling of German Compounds Jan A. Botha, Chris Dyer and Phil Blunsom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341 . . . . . . . . . . . . . . . . . . . . 357 Identification of Social Acts in Dialogue David Bracewell, Marc Tomlinson and Hui Wang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375 Robust, Lexicalized Native Language Identification Julian Brooke and Graeme Hirst . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391 Serhiy Bykh and Detmar Meurers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425 Analysis and Enhancement of Wikification for Microblogs with Context Expansion Taylor Cassidy, Heng Ji, Lev-Arie Ratinov, Arkaitz Zubiaga and Hongzhao Huang . . . 441 On the Effectiveness of using Sentence Compression Models for Query-Focused Multi-Document Summarization Yllias Chali and Sadid A. Hasan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457 Towards Automatic Topical Question Generation Yllias Chali and Sadid A. Hasan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475 Adjective Deletion for Linguistic Steganography and Secret Sharing Ching-Yun Chang and Stephen Clark . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493", | |
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