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| "text": "Welcome to TextGraphs, the Workshop on Graph-Based Methods for Natural Language Processing. The fifteenth edition of our workshop is being organized online on June 11, 2021, in conjunction with the 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-2021).", | |
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| "section": "Introduction", | |
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| "text": "The workshops in the TextGraphs series have published and promoted the synergy between the field of Graph Theory (GT) and Natural Language Processing (NLP). The mix between the two started small, with graph theoretical frameworks providing efficient and elegant solutions for NLP applications. Graphbased solutions initially focused on single-document part-of-speech tagging, word sense disambiguation, and semantic role labeling, and became progressively larger to include ontology learning and information extraction from large text collections. Nowadays, graph-based solutions also target on Web-scale applications such as information propagation in social networks, rumor proliferation, e-reputation, multiple entity detection, language dynamics learning, and future events prediction, to name a few.", | |
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| "section": "Introduction", | |
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| "text": "The target audience comprises researchers working on problems related to either Graph Theory or graphbased algorithms applied to Natural Language Processing, Social Media, and the Semantic Web.", | |
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| "section": "Introduction", | |
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| "text": "This year, we received 22 submissions and accepted 17 of them for oral presentation (12 long papers and 5 short papers). Similarly to the last year, we organized a shared task on Multi-Hop Inference for Explanation Regeneration. The goal of the task was to provide detailed gold explanations for standardized elementary science exam questions by selecting facts from a knowledge base. This year's shared task on multi-hop explanation regeneration attracted four teams. Three participants' reports along with the shared task overview by its organizers are also presented at the workshop.", | |
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| "section": "Introduction", | |
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| "text": "We would like to thank our invited speakers Laura Dietz (University of New Hampshire) and Jure Leskovec (Stanford University) for their talks. We are also thankful to the members of the program committee for their valuable and high-quality reviews. All submissions have benefited from their expert feedback. Their timely contribution was the basis for accepting an excellent list of papers and making the fourteenth edition of TextGraphs a success. ", | |
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| "last": "Dufter ; Manling", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Tuan", | |
| "middle": [], | |
| "last": "Li", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Lai", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ji ; Ting-Hao", | |
| "middle": [], | |
| "last": "Heng", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Rebecca", | |
| "middle": [ | |
| "J" | |
| ], | |
| "last": "Huang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "; Guangtao", | |
| "middle": [], | |
| "last": "Wang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Peng", | |
| "middle": [], | |
| "last": "Qi", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2021, | |
| "venue": "Bootstrapping Large-Scale Fine-Grained Contextual Advertising Classifier from Wikipedia Yiping Jin, Vishakha Kadam and Dittaya Wanvarie", | |
| "volume": "11", | |
| "issue": "", | |
| "pages": "50--69", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Workshop Program Friday, June 11, 2021 10:00-10:15 Opening Session 10:15-11:15 Invited Talk by Prof. Jure Leskovec (Stanford University) 11:15-11:30 Break 11:30-13:10 Oral Presentation Session -1 11:30-11:50 Bootstrapping Large-Scale Fine-Grained Contextual Advertising Classifier from Wikipedia Yiping Jin, Vishakha Kadam and Dittaya Wanvarie 11:50-12:10 Modeling Graph Structure via Relative Position for Text Generation from Knowl- edge Graphs Martin Schmitt, Leonardo F. R. Ribeiro, Philipp Dufter, Iryna Gurevych and Hinrich Sch\u00fctze 12:10-12:30 Entity Prediction in Knowledge Graphs with Joint Embeddings Matthias Baumgartner, Daniele Dell'Aglio and Abraham Bernstein 12:30-12:50 Hierarchical Graph Convolutional Networks for Jointly Resolving Cross-document Coreference of Entity and Event Mentions Duy Phung, Tuan Ngo Nguyen and Thien Huu Nguyen 12:50-13:10 GENE: Global Event Network Embedding Qi Zeng, Manling Li, Tuan Lai, Heng Ji, Mohit Bansal and Hanghang Tong 13:10-13:25 Break Friday, June 11, 2021 (continued) 13:25-15:25 Oral Presentation Session -2 13:25-13:45 Learning Clause Representation from Dependency-Anchor Graph for Connective Prediction Yanjun Gao, Ting-Hao Huang and Rebecca J. Passonneau 13:45-14:05 WikiGraphs: A Wikipedia Text -Knowledge Graph Paired Dataset Luyu Wang, Yujia Li, Ozlem Aslan and Oriol Vinyals 14:05-14:20 Selective Attention Based Graph Convolutional Networks for Aspect-Level Senti- ment Classification Xiaochen Hou, Jing Huang, Guangtao Wang, Peng Qi, Xiaodong He and Bowen Zhou 14:20-14:45 Keyword Extraction Using Unsupervised Learning on the Document's Adjacency Matrix Eirini Papagiannopoulou, Grigorios Tsoumakas and Apostolos Papadopoulos 14:45-15:05 Improving Human Text Simplification with Sentence Fusion Max Schwarzer, Teerapaun Tanprasert and David Kauchak 15:05-15:25 Structural Realization with GGNNs Jinman Zhao, Gerald Penn and huan ling 15:25-15:40 Break x Friday, June 11, 2021 (continued) 15:40-16:40 Invited Talk by Prof. Laura Dietz (University of New Hampshire) 16:40-16:50 Break 16:50-18:05 Oral Presentation Session -3 16:50-17:05 MG-BERT: Multi-Graph Augmented BERT for Masked Language Modeling Parishad BehnamGhader, Hossein Zakerinia and Mahdieh Soleymani Baghshah 17:05-17:20 GTN-ED: Event Detection Using Graph Transformer Networks Sanghamitra Dutta, Liang Ma, Tanay Kumar Saha, Di Liu, Joel Tetreault and Ale- jandro Jaimes 17:20-17:35 Fine-grained General Entity Typing in German using GermaNet Sabine Weber and Mark Steedman 17:35-17:50 On Geodesic Distances and Contextual Embedding Compression for Text Classifi- cation Rishi Jha and Kai Mihata 17:50-18:05 Semi-Supervised Joint Estimation of Word and Document Readability Yoshinari Fujinuma and Masato Hagiwara 18:05-18:10 Break Friday, June 11, 2021 (continued) 18:10-18:50 Poster Session 18:10-18:50 TextGraphs 2021 Shared Task on Multi-Hop Inference for Explanation Regeneration Peter Jansen, Mokanarangan Thayaparan, Marco Valentino and Dmitry Ustalov 18:10-18:50 DeepBlueAI at TextGraphs 2021 Shared Task: Treating Multi-Hop Inference Expla- nation Regeneration as A Ranking Problem Chunguang Pan, Bingyan Song and Zhipeng Luo 18:10-18:50 A Three-step Method for Multi-Hop Inference Explanation Regeneration Yuejia Xiang, Yunyan Zhang, Xiaoming Shi, Bo Liu, Wandi Xu and Xi Chen 18:10-18:50 Textgraphs-15 Shared Task System Description : Multi-Hop Inference Explanation Regeneration by Matching Expert Ratings Sureshkumar Vivek Kalyan, Sam Witteveen and Martin Andrews 18:50-19:00 Closing Remarks", | |
| "links": null | |
| } | |
| }, | |
| "ref_entries": { | |
| "TABREF0": { | |
| "num": null, | |
| "content": "<table><tr><td>DeepBlueAI at TextGraphs 2021 Shared Task: Treating Multi-Hop Inference Explanation Regeneration</td></tr><tr><td>as A Ranking Problem</td></tr><tr><td>Chunguang Pan,</td></tr><tr><td>Alexander Panchenko, Fragkiskos D. Malliaros, Varvara Logacheva, Abhik Jana, Dmitry Ustalov, Peter</td></tr><tr><td>Jansen.</td></tr><tr><td>TextGraphs-15 Organizers</td></tr><tr><td>June 2021</td></tr></table>", | |
| "text": "Bingyan Song and Zhipeng Luo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 A Three-step Method for Multi-Hop Inference Explanation Regeneration Yuejia Xiang, Yunyan Zhang, Xiaoming Shi, Bo Liu, Wandi Xu and Xi Chen . . . . . . . . . . . . . . . . 171 Textgraphs-15 Shared Task System Description : Multi-Hop Inference Explanation Regeneration by Matching Expert Ratings Sureshkumar Vivek Kalyan, Sam Witteveen and Martin Andrews . . . . . . . . . . . . . . . . . . . . . . . . . . . 176", | |
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