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| "paper_id": "K15-2000", | |
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| "text": "This volume contains papers describing the CoNLL-2015 Shared Task and the participating systems. This year, we continue the tradition of the Conference on Computational Natural Language Learning (CoNLL) of having a high profile shared task in Natural Language Processing (NLP), focusing on Shallow Discourse Parsing, which involves identifying individual discourse relations that are present in a natural language text. A discourse relation can be expressed explicitly or implicitly, and takes two arguments realized as sentences, clauses, or in some rare cases, phrases. Shallow Discourse Parsing is a fundamental NLP task and can potentially benefit a range of natural language applications such as Information Extraction, Text Summarization, Question Answering, Machine Translation, and Sentiment Analysis.", | |
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| "section": "Introduction", | |
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| "text": "A total of sixteen teams from three continents participated in this task, and fourteen of them submitted system description papers. Many different approaches were adopted by the participants, and we hope that these approaches help to advance the state of the art in Shallow Discourse Parsing. The training, development, and test sets were adapted from the Penn Discourse TreeBank (PDTB). In addition, we also annotated a blind test set following the PDTB guidelines solely for the shared task. The results on the blind test set were used to rank the participating systems. The evaluation scorer, also developed for this shared task, adopts an F1 based metric that takes into account the accuracy of identifying the senses and arguments of discourse relations as well as explicit discourse connectives. We hope that the data sets and the scorer, which are freely available upon the completion of the shared task, will be a useful resource for researchers interested in discourse parsing.", | |
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| "section": "Introduction", | |
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| "text": "For the first time in the history of the CoNLL shared tasks, participating teams, instead of running their systems on the test set and submitting the output, were asked to deploy their systems on a remote virtual machine and use a web-based evaluation platform to run their systems on the test set. This meant they were unable to actually see the data set, thus preserving its integrity and ensuring its replicability. We hope that the successful implementation of this new evaluation protocol in the shared task will encourage its adoption in future NLP evaluation campaigns. ", | |
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| "section": "Introduction", | |
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| "text": "The CoNLL-2015 Shared Task on Shallow Discourse Parsing Nianwen Xue, Hwee Tou Ng, Sameer Pradhan, Rashmi Prasad, Christopher Bryant and Attapol Rutherford . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 A Refined End-to-End Discourse Parser Jianxiang Wang and Man Lan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 The UniTN Discourse Parser in CoNLL 2015 Shared Task: Token-level Sequence Labeling with Argumentspecific Models Evgeny Stepanov, Giuseppe Riccardi and Ali Orkan Bayer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 The SoNLP-DP System in the CoNLL-2015 shared Task Fang Kong, Sheng Li and Guodong Zhou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Shallow Discourse Parsing Using Constituent Parsing Tree Changge Chen, Peilu Wang and Hai Zhao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 A Minimalist Approach to Shallow Discourse Parsing and Implicit Relation Recognition Christian Chiarcos and Niko Schenk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 A Hybrid Discourse Relation Parser in CoNLL 2015 Sobha Lalitha Devi, Sindhuja Gopalan, Lakshmi S, Pattabhi RK Rao, Vijay Sundar Ram and Malarkodi C.S. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 The CLaC Discourse Parser at CoNLL-2015 Majid Laali, Elnaz Davoodi and Leila Kosseim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 Shallow Discourse Parsing with Syntactic and (a Few) Semantic Features Shubham Mukherjee, Abhishek Tiwari, Mohit Gupta and Anil Kumar Singh . . . . . . . . . . . . . . . . . . 61 JAIST: A two-phase machine learning approach for identifying discourse relations in newswire texts Son Nguyen, Quoc Ho and Minh Nguyen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 The DCU Discourse Parser: A Sense Classification Task Tsuyoshi Okita, Longyue Wang and Qun Liu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 Improving a Pipeline Architecture for Shallow Discourse Parsing Yangqiu Song, Haoruo Peng, Parisa Kordjamshidi, Mark Sammons and Dan Roth . . . . . . . . . . . . . 78 A Shallow Discourse Parsing System Based On Maximum Entropy Model Jia Sun, Peijia Li, Weiqun Xu and Yonghong Yan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 The DCU Discourse Parser for Connective, Argument Identification and Explicit Sense Classification Longyue Wang, Chris Hokamp, Tsuyoshi Okita, Xiaojun Zhang and Qun Liu . . . . . . . . . . . . . . . . . 89 Hybrid Approach to PDTB-styled Discourse Parsing for CoNLL-2015 Yasuhisa Yoshida, Katsuhiko Hayashi, Tsutomu Hirao and Masaaki Nagata . . . . . . . . . . . . . . . . . . 95", | |
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