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Your search keyword '"Muthu Kumar Chandrasekaran"' showing total 4 results
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4 results on '"Muthu Kumar Chandrasekaran"'

1. Report on the 3rd Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2018)

Author

Philipp Mayr, Muthu Kumar Chandrasekaran, and Kokil Jaidka

Subjects
FOS: Computer and information sciences, Hardware and Architecture, 05 social sciences, Digital Libraries (cs.DL), Computer Science - Digital Libraries, 0509 other social sciences, 050905 science studies, 050904 information & library sciences, Information Retrieval (cs.IR), Management Information Systems, Computer Science - Information Retrieval
Abstract

The $3^{rd}$ joint BIRNDL workshop was held at the 41st ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018) in Ann Arbor, USA. BIRNDL 2018 intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The workshop incorporated three paper sessions and the $4^{th}$ edition of the CL-SciSumm Shared Task., 6 pages, to appear in SIGIR Forum

Published
2018

2. Using Discourse Signals for Robust Instructor Intervention Prediction

Author

Muthu Kumar Chandrasekaran, Carrie Epp, Min-Yen Kan, and Diane Litman

Subjects
FOS: Computer and information sciences, Computer Science - Computers and Society, Artificial Intelligence (cs.AI), Computer Science - Computation and Language, Computer Science - Artificial Intelligence, I.2.7, Computers and Society (cs.CY), K.3.1, General Medicine, Computation and Language (cs.CL)
Abstract

We tackle the prediction of instructor intervention in student posts from discussion forums in Massive Open Online Courses (MOOCs). Our key finding is that using automatically obtained discourse relations improves the prediction of when instructors intervene in student discussions, when compared with a state-of-the-art, feature-rich baseline. Our supervised classifier makes use of an automatic discourse parser which outputs Penn Discourse Treebank (PDTB) tags that represent in-post discourse features. We show PDTB relation-based features increase the robustness of the classifier and complement baseline features in recalling more diverse instructor intervention patterns. In comprehensive experiments over 14 MOOC offerings from several disciplines, the PDTB discourse features improve performance on average. The resultant models are less dependent on domain-specific vocabulary, allowing them to better generalize to new courses., To appear in proceedings of the 31st AAAI Conference on Artificial Intelligence, San Francisco, USA

Published
2016

3. Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2016)

Author

Muthu Kumar Chandrasekaran, Guillaume Cabanac, Kokil Jaidka, Ingo Frommholz, Min-Yen Kan, Dietmar Wolfram, Philipp Mayr, Recherche d’Information et Synthèse d’Information (IRIT-IRIS), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Université Toulouse III - Paul Sabatier (UT3), National University of Singapore (NUS), University of Bedfordshire, Adobe Systems Inc., Leibniz-Institute for the Social Sciences [Mannheim] (GESIS ), UFR Santé, Médecine et Biologie Humaine, Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Adobe System (USA), Leibniz Institute for the Social Sciences - GESIS (GERMANY), National University of Singapore - NUS (REPUBLIC OF SINGAPORE), University of Wisconsin - Milwaukee (USA), University of Bedfordshire (UNITED KINGDOM), and Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)

Subjects
Text mining, Computer science, Bibliometrics, 050905 science studies, computer.software_genre, Information retrieval, Théorie de l'information, Digital libraries, business.industry, Scale (chemistry), Natural language processing, 05 social sciences, Recherche d'information, Sensemaking, Digital library, Metadata, [INFO.INFO-IT]Computer Science [cs]/Information Theory [cs.IT], [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Artificial intelligence, 0509 other social sciences, Computational linguistics, 050904 information & library sciences, business, computer
Abstract

International audience; The large scale of scholarly publications poses a challenge for scholars in information-seeking and sensemaking. Bibliometric, information retrieval (IR), text mining and NLP techniques could help in these activities, but are not yet widely used in digital libraries. This workshop is intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometric and recommendation techniques which can advance the state-of-the-art in scholarly document understanding, analysis and retrieval at scale.

Published
2016

4. Identifying the Conceptual Space of Citation Contexts using Coreferences

Author

Marc Bertin, Pierre Jonin, Frédéric Armetta, Iana Atanassova, Equipe de recherche de Lyon en sciences de l'information et de la communication (ELICO), Sciences Po Lyon - Institut d'études politiques de Lyon (IEP Lyon), Université de Lyon-Université de Lyon-École nationale supérieure des sciences de l'information et des bibliothèques (ENSSIB), Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université Lumière - Lyon 2 (UL2), Systèmes Cognitifs et Systèmes Multi-Agents (SyCoSMA), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Centre de recherches interdisciplinaires et transculturelles - UFC (EA 3224) (CRIT), Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC), SIGIR, Muthu Kumar Chandrasekaran, Philipp Mayr, Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Centre de recherches interdisciplinaires et transculturelles - UFC (UR 3224) (CRIT), Bertin, Marc, Université Lumière - Lyon 2 (UL2)-École nationale supérieure des sciences de l'information et des bibliothèques (ENSSIB), Université de Lyon-Université de Lyon-Sciences Po Lyon - Institut d'études politiques de Lyon (IEP Lyon), Université de Lyon, Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, Coreference Resolution, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], [SHS.INFO]Humanities and Social Sciences/Library and information sciences, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], [INFO.INFO-TT] Computer Science [cs]/Document and Text Processing, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Citation contexts, Deep learning, [SHS.INFO] Humanities and Social Sciences/Library and information sciences, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; The study of citation contexts is an important element in understanding the function of citations and categorizing the relationshipsbetween works. One of the problems in this field is defining the size of citation contexts. In this paper we propose the definition of citationblocks (CB) that are citation contexts composed of one or more sentences that are linked by coreference clusters. We describe the methodology forthe automatic processing and determining the boundaries of CB and observe the different sizes of CB in the different sections of the IMRaDstructure of articles. The results are obtained from a sample of 70,000 citation contexts extracted from the PLOS dataset.

Published
2019

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