{ "paper_id": "S15-1000", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T15:36:48.928514Z" }, "title": "*SEM 2015 Chairs and Reviewers General Chair", "authors": [ { "first": "Martha", "middle": [], "last": "Palmer", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Gemma", "middle": [], "last": "Boleda", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Paolo", "middle": [], "last": "Rosso", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Marc", "middle": [], "last": "Franco-Salvador", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Edward", "middle": [], "last": "Grefenstette", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Google", "middle": [], "last": "Deepmind", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Hwee", "middle": [ "Tou" ], "last": "Ng", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Paul", "middle": [], "last": "Buitelaar", "suffix": "", "affiliation": {}, 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{}, "email": "" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "", "pdf_parse": { "paper_id": "S15-1000", "_pdf_hash": "", "abstract": [], "body_text": [ { "text": "The Joint Conference on Lexical and Computational Semantics (*SEM) provides a forum of exchange for the growing number of NLP researchers working on different aspects of semantic processing. After the previous editions of * SEM in Montreal (2012) , Atlanta (2013) , and Dublin (2014), the 2015 edition will take place in Denver on June 4 and 5 and is colocated with SemEval and NAACL. As in 2014 at COLING, also on this occasion *SEM and SemEval chose to coordinate their programs by featuring a joint invited talk. In this way, *SEM aims to bring together the ACL SIGLEX and ACL SIGSEM communities.", "cite_spans": [ { "start": 224, "end": 246, "text": "SEM in Montreal (2012)", "ref_id": null }, { "start": 249, "end": 263, "text": "Atlanta (2013)", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "*SEM 2015: Joint Conference on Lexical and Computational Semantics", "sec_num": null }, { "text": "The acceptance rate of *SEM 2015 was quite competitive: out of 98 submissions, we accepted 36 papers for an overall acceptance of 37%. The acceptance rate of long paper that were accepted for oral presentation (18 out of 62) is 29%. The papers cover a wide range of topics including distributional semantics; lexical semantics and lexical acquisition; formal and linguistic semantics; discourse semantics; lexical resources, linked data and ontologies; semantics for applications; and extra-propositional semantics: sentiment and figurative meaning.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "*SEM 2015: Joint Conference on Lexical and Computational Semantics", "sec_num": null }, { "text": "The *SEM 2015 program consists of oral presentations for selected long papers and a poster session for long and short papers.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "*SEM 2015: Joint Conference on Lexical and Computational Semantics", "sec_num": null }, { "text": "Day One, June 4th:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "*SEM 2015: Joint Conference on Lexical and Computational Semantics", "sec_num": null }, { "text": "\u2022 Joint *SEM SemEval keynote talk by Marco Baroni;", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "*SEM 2015: Joint Conference on Lexical and Computational Semantics", "sec_num": null }, { "text": "\u2022 Oral presentation sessions on distributional semantics, lexical semantics, and extra-propositional semantics;", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "*SEM 2015: Joint Conference on Lexical and Computational Semantics", "sec_num": null }, { "text": "\u2022 Poster session.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "*SEM 2015: Joint Conference on Lexical and Computational Semantics", "sec_num": null }, { "text": "Day Two, June 5th:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "*SEM 2015: Joint Conference on Lexical and Computational Semantics", "sec_num": null }, { "text": "\u2022 Keynote talk by Preslav Natkov;", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "*SEM 2015: Joint Conference on Lexical and Computational Semantics", "sec_num": null }, { "text": "\u2022 Oral presentation sessions on semantics for applications, lexical resources and ontologies, formal semantics, and discourse semantics;", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "*SEM 2015: Joint Conference on Lexical and Computational Semantics", "sec_num": null }, { "text": "\u2022 *SEM Best Paper Award.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "*SEM 2015: Joint Conference on Lexical and Computational Semantics", "sec_num": null }, { "text": "We cannot finish without saying that *SEM 2015 would not have been possible without the considerable efforts of our area chairs, their reviewers, and the computational semantics community in general. Distributional semantic methods have some a priori appeal as models of human meaning acquisition, because they induce word representations from contextual distributions naturally occurring in corpus data without need for supervision. However, learning the meaning of a (concrete) word also involves establishing a link between the word and its typical visual referents, which is beyond the scope of classic, text-based distributional semantics. Since recently several proposals have been put forward about how to induce multimodal word representations from linguistic and visual contexts, it is natural to ask if this line of work, besides its practical implications, can help us to develop more realistic, grounded models of human word learning within the distributional semantics framework.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "*SEM 2015: Joint Conference on Lexical and Computational Semantics", "sec_num": null }, { "text": "In my talk, I will report about two studies in which we used multimodal distributional semantics (MDS) to simulate human word learning. In one study, we first measured the ability of subjects to link a nonce word to relevant linguistic and visual associates when prompted only by exposure to minimal corpus evidence about it. We then simulated the same task with an MDS model, finding its behavior remarkably similar to that of subjects. In the second study, we constructed a corpus in which child-directed speech is aligned with real-life pictures of the objects mentioned by care-givers. We then trained our MDS model on these data, and inspected the generalizations it learned about the words in the corpus and the objects they might denote.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "*SEM 2015: Joint Conference on Lexical and Computational Semantics", "sec_num": null }, { "text": "The results highlight interesting issues not only for distributional semantics (can we build meaningful word representations from very limited contexts? are such representations reasonably human-like?), but also for the study of human language acquisition (are we \"done\" with learning a word once we associate it to a referent? do we incrementally refine our word representations? is an explicit cross-situational mechanism really necessary?).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "*SEM 2015: Joint Conference on Lexical and Computational Semantics", "sec_num": null }, { "text": "xvi", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "*SEM 2015: Joint Conference on Lexical and Computational Semantics", "sec_num": null } ], "back_matter": [ { "text": "We hope you will enjoy *SEM 2015, Martha Palmer, University of Colorado Boulder, General Chair Gemma Boleda, University of Trento, Program Co-Chair Paolo Rosso, Universitat Polit\u00e8cnica de Val\u00e8ncia, Program Co-Chair", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "acknowledgement", "sec_num": null }, { "text": "The 60-year-old dream of computational linguistics is to make computers capable of communicating with humans in natural language. This has proven hard, and thus research has focused on sub-problems. Even so, the field was stuck with manual rules until the early 90s, when computers became powerful enough to enable the rise of statistical approaches. Eventually, this shifted the main research attention to machine learning from text corpora, thus triggering a revolution in the field.Today, the Web is the biggest available corpus, providing access to quadrillions of words; and, in corpusbased natural language processing, size does matter. Unfortunately, while there has been substantial research on the Web as a corpus, it has typically been restricted to using page hit counts as an estimate for n-gram word frequencies; this has led some researchers to conclude that the Web should be only used as a baseline.In this talk, I will reveal some of the hidden potential of the Web that lies beyond the n-gram, with focus on the syntax and semantics of English noun compounds. I will further show how these ideas apply to a number of NLP problems, including syntactic parsing and machine translation, among others. Finally, I will share some thoughts about the future of lexical semantics and machine translation, in view of the ongoing deep learning revolution.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Conference Program", "sec_num": null } ], "bib_entries": { "BIBREF0": { "ref_id": "b0", "title": "Towards Semantic Language Classification: Inducing and Clustering Semantic Association Networks from Europarl Steffen Eger", "authors": [], "year": null, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Towards Semantic Language Classification: Inducing and Clustering Semantic Association Networks from Europarl Steffen Eger, Niko Schenk and Alexander Mehler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .127", "links": null }, "BIBREF1": { "ref_id": "b1", "title": "Ideological Perspective Detection Using Semantic Features Heba Elfardy, Mona Diab and Chris Callison-Burch", "authors": [], 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Hwang and Martha Palmer 15:00-15:30 A Methodology for Word Sense Disambiguation at 90% based on large-scale CrowdSourcing Oier Lopez de Lacalle and Eneko Agirre 15:30-16:00 Coffee break 16:00-17:00 Block 4 -Extra-propositional semantics Session Chair: Tony Veale 16:00-16:30 Learning Structures of Negations from Flat Annotations Vinodkumar Prabhakaran and Branimir Boguraev", "authors": [ { "first": "Michael", "middle": [], "last": "Mesgar", "suffix": "" }, { "first": "", "middle": [ ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ; Julia" ], "last": "Strube", "suffix": "" }, { "first": "Owen", "middle": [], "last": "Hirschberg", "suffix": "" }, { "first": "Samira", "middle": [], "last": "Rambow", "suffix": "" }, { "first": "Tomek", "middle": [], "last": "Shaikh", "suffix": "" }, { "first": "Jennifer", "middle": [], "last": "Strzalkowski", "suffix": "" }, { "first": "Michael", "middle": [], "last": "Tracey", "suffix": "" }, { "first": "Rupayan", "middle": [], "last": "Arrigo", "suffix": "" }, { "first": "Micah", "middle": [], "last": "Basu", "suffix": "" }, { "first": "Adam", "middle": [], "last": "Clark", "suffix": "" }, { "first": "Mona", "middle": [], "last": "Dalton", "suffix": "" }, { "first": "Louise", "middle": [], "last": "Diab", "suffix": "" }, { "first": "Anna", "middle": [], "last": "Guthrie", "suffix": "" }, { "first": "Stephanie", "middle": [], "last": "Prokofieva", "suffix": "" }, { "first": "Gregory", "middle": [], "last": "Strassel", "suffix": "" }, { "first": "Yorick", "middle": [], "last": "Werner", "suffix": "" }, { "first": "Janyce", "middle": [], "last": "Wilks", "suffix": "" }, { "first": "", "middle": [], "last": "Wiebe", "suffix": "" } ], "year": null, "venue": "A State-of-the-Art Mention-Pair Model for Coreference Resolution Olga Uryupina and Alessandro Moschitti", "volume": "11", "issue": "", "pages": "30--47", "other_ids": {}, "num": null, "urls": [], "raw_text": "Graph-based Coherence Modeling For Assessing Readability Mohsen Mesgar and Michael Strube . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310 10:30-11:00 Coffee break 11:00-12:30 Block 2 -Distributional semantics Session Chair: Ed Grefenstette 11:00-11:30 Compositional Distributional Semantics with Long Short Term Memory Phong Le and Willem Zuidema 11:30-12:00 A Hybrid Distributional and Knowledge-based Model of Lexical Semantics Nikolaos Aletras and Mark Stevenson 12:00-12:30 Distributional semantics for ontology verification Julien Corman, Laure Vieu and Nathalie Aussenac-Gilles 12:30-14:00 Lunch break Thursday, June 4 (continued) 14:00-15:30 Block 3 -Lexical semantics Session Chair: Gemma Boleda 14:00-14:30 Combining Seemingly Incompatible Corpora for Implicit Semantic Role Labeling Parvin Sadat Feizabadi and Sebastian Pad\u00f3 14:30-15:00 Identification of Caused Motion Construction Jena D. Hwang and Martha Palmer 15:00-15:30 A Methodology for Word Sense Disambiguation at 90% based on large-scale CrowdSourcing Oier Lopez de Lacalle and Eneko Agirre 15:30-16:00 Coffee break 16:00-17:00 Block 4 -Extra-propositional semantics Session Chair: Tony Veale 16:00-16:30 Learning Structures of Negations from Flat Annotations Vinodkumar Prabhakaran and Branimir Boguraev 16:30-17:00 A New Dataset and Evaluation for Belief/Factuality Vinodkumar Prabhakaran, Tomas By, Julia Hirschberg, Owen Rambow, Samira Shaikh, Tomek Strzalkowski, Jennifer Tracey, Michael Arrigo, Rupayan Basu, Micah Clark, Adam Dalton, Mona Diab, Louise Guthrie, Anna Prokofieva, Stephanie Strassel, Gregory Werner, Yorick Wilks and Janyce Wiebe 17:00-19:00 Poster session with lightning talks intro Non-Orthogonal Explicit Semantic Analysis Nitish Aggarwal, Kartik Asooja, Georgeta Bordea and Paul Buitelaar Combining Mention Context and Hyperlinks from Wikipedia for Named Entity Dis- ambiguation Ander Barrena, Aitor Soroa and Eneko Agirre Collective Document Classification with Implicit Inter-document Semantic Rela- tionships Clint Burford, Steven Bird and Timothy Baldwin Thursday, June 4 (continued) Learning to predict script events from domain-specific text Rachel Rudinger, Vera Demberg, Ashutosh Modi, Benjamin Van Durme and Man- fred Pinkal Combining Open Source Annotators for Entity Linking through Weighted Voting Pablo Ruiz and Thierry Poibeau Automatic Generation of a Lexical Resource to support Semantic Role Labeling in Portuguese Magali Sanches Duran and Sandra Alu\u00edsio Can Selectional Preferences Help Automatic Semantic Role Labeling? Shumin Wu and Martha Palmer Friday, June 5 09:00-10:30 Block 1 -Semantics for applications Session Chair: Paolo Rosso 09:00-10:00 60 Years Ago People Dreamed of Talking with a Machine. Are We Any Closer? Keynote by Preslav Nakov 10:00-10:30 Implicit Entity Recognition in Clinical Documents Sujan Perera, Pablo Mendes, Amit Sheth, Krishnaprasad Thirunarayan, Adarsh Alex, Christopher Heid and Greg Mott 10:30-11:00 Coffee break 11:00-12:00 Block 2 -Semantics for applications; Lexical resources and ontologies Session Chair: Alessandro Moschitti 11:00-11:30 A Distant Supervision Approach to Semantic Role Labeling Peter Exner, Marcus Klang and Pierre Nugues 11:30-12:00 Discovering Hypernymy Relations using Text Layout Jean-Philippe Fauconnier and Mouna Kamel 12:00-13:30 Lunch break Friday, June 5 (continued) 13:30-15:00 Block 3 -Formal semantics Session Chair: TBD 13:30-14:00 The complexity of finding the maximum spanning DAG and other restrictions for DAG parsing of natural language Natalie Schluter 14:00-14:30 Incremental Semantic Construction Using Normal Form CCG Derivation Yoshihide Kato and Shigeki Matsubara 14:30-15:00 Dependency-Based Semantic Role Labeling using Convolutional Neural Networks William Foland and James Martin 15:00-15:30 Coffee break 15:30-17:00 Block 4 -Discourse semantics Session Chair: TBD 15:30-16:00 A State-of-the-Art Mention-Pair Model for Coreference Resolution Olga Uryupina and Alessandro Moschitti 16:00-16:30 Resolving Discourse-Deictic Pronouns: A Two-Stage Approach to Do It Sujay Kumar Jauhar, Raul Guerra, Edgar Gonz\u00e0lez Pellicer and Marta Recasens 16:30-17:00 Graph-based Coherence Modeling For Assessing Readability Mohsen Mesgar and Michael Strube 17:00 Best Paper Award and closing", "links": null } }, "ref_entries": { "TABREF0": { "content": "
A New Dataset and Evaluation for Belief/Factuality Vinodkumar Prabhakaran, Tomas By, Julia Hirschberg, Owen Rambow, Samira Shaikh, Tomek Strzalkowski, Jennifer Tracey, Michael Arrigo, Rupayan Basu, Micah Clark, Adam Dalton, Mona Diab, Playing ficles and running with the corbons: What (multimodal) distributional semantic models learn during their childhood Marco Baroni, University of Trento Joint work with: Angeliki Lazaridou, Marco Marelli (University of Trento), Louise Guthrie, Invited Talks Raquel Fernandez (University of Amsterdam), Grzegorz Chrupa\u0142a (Tilburg University)
", "type_str": "table", "num": null, "text": "Neural Networks for Integrating Compositional and Non-compositional Sentiment in Sentiment Composition Xiaodan Zhu, Hongyu Guo and Parinaz Sobhani . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Compositional Distributional Semantics with Long Short Term Memory Phong Le and Willem Zuidema . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 A Hybrid Distributional and Knowledge-based Model of Lexical Semantics Nikolaos Aletras and Mark Stevenson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Distributional semantics for ontology verification Julien Corman, Laure Vieu and Nathalie Aussenac-Gilles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Combining Seemingly Incompatible Corpora for Implicit Semantic Role Labeling Parvin Sadat Feizabadi and Sebastian Pad\u00f3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Identification of Caused Motion Construction Jena D. Hwang and Martha Palmer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 A Methodology for Word Sense Disambiguation at 90% based on large-scale CrowdSourcing Oier Lopez de Lacalle and Eneko Agirre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Learning Structures of Negations from Flat Annotations Vinodkumar Prabhakaran and Branimir Boguraev . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 Anna Prokofieva, Stephanie Strassel, Gregory Werner, Yorick Wilks and Janyce Wiebe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 Non-Orthogonal Explicit Semantic Analysis Nitish Aggarwal, Kartik Asooja, Georgeta Bordea and Paul Buitelaar . . . . . . . . . . . . . . . . . . . . . . . 92 Combining Mention Context and Hyperlinks from Wikipedia for Named Entity Disambiguation Ander Barrena, Aitor Soroa and Eneko Agirre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Collective Document Classification with Implicit Inter-document Semantic Relationships Clint Burford, Steven Bird and Timothy Baldwin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 SGRank: Combining Statistical and Graphical Methods to Improve the State of the Art in Unsupervised Keyphrase Extraction Soheil Danesh, Tamara Sumner and James H. Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117", "html": null } } } }