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| "paper_id": "2020", |
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| { |
| "text": "The Joint Workshop on Multiword Expressions and Electronic Lexicons (MWE-LEX 2020) 1 took place in an online format on December 13, 2020 in conjunction with COLING 2020.", |
| "cite_spans": [], |
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| "section": "Introduction", |
| "sec_num": null |
| }, |
| { |
| "text": "This was the 16th edition of the Workshop on Multiword Expressions (MWE 2020). The event was organized and sponsored by the Special Interest Group on the Lexicon (SIGLEX) 2 of the Association for Computational Linguistics (ACL) and by ELEXIS 3 -European Lexicographic Infrastructure.", |
| "cite_spans": [], |
| "ref_spans": [], |
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| "section": "Introduction", |
| "sec_num": null |
| }, |
| { |
| "text": "The joint MWE-LEX workshop addressed two domains -multiword expressions and (electronic) lexicons -with partly overlapping communities and research interests, but divergent practices and terminologies.", |
| "cite_spans": [], |
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| "section": "Introduction", |
| "sec_num": null |
| }, |
| { |
| "text": "Multiword expressions (MWEs) are word combinations, such as in the middle of nowhere, hot dog, to make a decision or to kick the bucket, displaying lexical, syntactic, semantic, pragmatic and/or statistical idiosyncrasies. Because of their unpredictable behavior, notably their non-compositional semantics, MWEs pose problems in linguistic modelling (e.g. treebank annotation, grammar engineering), NLP pipelines (notably when orchestrated with parsing), and end-user applications (e.g. information extraction).", |
| "cite_spans": [], |
| "ref_spans": [], |
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| "section": "Introduction", |
| "sec_num": null |
| }, |
| { |
| "text": "Because MWE-hood is a largely lexical phenomenon, appropriately built electronic MWE lexicons turn out to be quite important for NLP. Large standardised multilingual, possibly interconnected, NLPoriented MWE lexicons prove indispensable for NLP tasks such as MWE identification, due to its critical sensitivity to unseen data. But the development of such lexicons is challenging and calls for tools which would leverage, on the one hand, MWEs encoded in pre-existing NLP-unaware lexicons and, on the other hand, automatic MWE discovery in large non-annotated corpora.", |
| "cite_spans": [], |
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| "section": "Introduction", |
| "sec_num": null |
| }, |
| { |
| "text": "In order to allow better convergence and scientific innovation within these two largely complementary scientific communities, we called for papers on joint topics on MWEs and e-lexicons, on the one hand, and on MWE-specific topics, on the other hand.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 Extracting and enriching MWE lists from traditional human-readable lexicons for NLP use", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Joint topics on MWEs and e-lexicons", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 Formats for NLP-applicable MWE lexicons", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Joint topics on MWEs and e-lexicons", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 Interlinking MWE lexicons with other language resources", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Joint topics on MWEs and e-lexicons", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 Using MWE lexicons in NLP tasks (identification, parsing, translation)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Joint topics on MWEs and e-lexicons", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 MWE discovery in the service of lexicography \u2022 Processing of MWEs and constructions in syntactic and semantic frameworks (e.g. CCG, CxG, HPSG, LFG, TAG, UD, etc.), and in end-user applications (e.g. information extraction, machine translation and summarization)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Joint topics on MWEs and e-lexicons", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 Original discovery and identification methods for MWEs and constructions", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Joint topics on MWEs and e-lexicons", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 MWEs and constructions in language acquisition and in non-standard language (e.g. tweets, forums, spontaneous speech)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Joint topics on MWEs and e-lexicons", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 Evaluation of annotation and processing techniques for MWEs and constructions", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Joint topics on MWEs and e-lexicons", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 Retrospective comparative analyses from the PARSEME shared tasks on automatic identification of MWEs", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Joint topics on MWEs and e-lexicons", |
| "sec_num": null |
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| { |
| "text": "We received 25 submissions (14 long and 11 short papers). We selected 6 long papers and 7 short ones. All 13 accepted papers were presented orally. The overall acceptance rate was 52 %.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Joint topics on MWEs and e-lexicons", |
| "sec_num": null |
| }, |
| { |
| "text": "In addition to the oral sessions, the workshop featured an invited talk that given by Roberto Navigli from Sapienza University of Rome.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Joint topics on MWEs and e-lexicons", |
| "sec_num": null |
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| { |
| "text": "The workshop also organized the PARSEME Shared Task on Semi-Supervised Identification of Verbal MWEs (edition 1.2). This was a follow-up of editions 1.0 (2017), and 1.1 (2018). Edition 1.2 featured (a) improved and extended corpora annotated with MWEs, (b) complementary unannotated corpora for unsupervised MWE discovery, and (c) a new evaluation methodology focusing on unseen MWEs. Following the synergy with Elexis, our aim was to foster the development of unsupervised methods for MWE lexicon induction, which in turn can be used for identification.", |
| "cite_spans": [], |
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| "section": "Joint topics on MWEs and e-lexicons", |
| "sec_num": null |
| }, |
| { |
| "text": "Seven teams submitted 9 system results to the shared task (some teams made 2 submissions): 2 to the closed track (where only the data provided by the organizers could be used on input) and 7 to the open track (where external data were also allowed). Out of these 9 results, 4 covered all 14 languages for which data were made available by the organizers. Six teams also submitted systems description papers, all of which were accepted and presented orally.", |
| "cite_spans": [], |
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| "section": "Joint topics on MWEs and e-lexicons", |
| "sec_num": null |
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| { |
| "text": "We are grateful to the paper authors for their valuable contributions, the members of the Program Committee for their thorough and timely reviews, all members of the organizing committee for the fruitful collaboration, and all the workshop participants for their interest in this event. Our thanks also go to the COLING 2020 organizers for their support, as well as to SIGLEX and ELEXIS for their endorsement.", |
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| "section": "Joint topics on MWEs and e-lexicons", |
| "sec_num": null |
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| { |
| "text": "Stella Markantonatou, John McCrae, Jelena Mitrovi\u0107, Carole Tiberiu, Carlos Ramisch, Ashwini Vaidya, Petya Osenova, Agata Savary", |
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| "section": "Joint topics on MWEs and e-lexicons", |
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| "raw_text": "Edition 1.2 of the PARSEME Shared Task on Semi-supervised Identification of Verbal Multiword Expres- sions Carlos Ramisch, Agata Savary, Bruno Guillaume, Jakub Waszczuk, Marie Candito, Ashwini Vaidya, Verginica Barbu Mititelu, Archna Bhatia, Uxoa I\u00f1urrieta, Voula Giouli, Tunga Gungor, Menghan Jiang, Timm Lichte, Chaya Liebeskind, Johanna Monti, Renata Ramisch, Sara Stymne, Abigail Walsh and Hongzhi Xu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107", |
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| "raw_text": "Sunday, December 13, 2020 14:00-14:10 Welcoming and preparation 14:10-14:40 Session 1: MWE Resources and Linguistics CollFrEn: Rich Bilingual English-French Collocation Resource Beatriz Fisas, Joan Codina-Filb\u00e1, Luis Espinosa Anke and Leo Wanner Filling the ___-s in Finnish MWE lexicons Frankie Robertson Hierarchy-aware Learning of Sequential Tool Usage via Semi-automatically Con- structed Taxonomies Nima Nabizadeh, Martin Heckmann and Dorothea Kolossa Scalar vs. mereological conceptualizations of the N-BY-N and NUM-BY-NUM ad- verbials Lucia Vl\u00e1\u0161kov\u00e1 and Mojm\u00edr Do\u010dekal 14:40-14:50 Break 14:50-15:20 Session 2: Verbal Multiword Expressions Polish corpus of verbal multiword expressions Agata Savary and Jakub Waszczuk AlphaMWE: Construction of Multilingual Parallel Corpora with MWE Annotations Lifeng Han, Gareth Jones and Alan Smeaton Annotating Verbal MWEs in Irish for the PARSEME Shared Task 1.2", |
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| "venue": "Session 5: Shared Task Edition 1.2 of the PARSEME Shared Task on Semi-supervised Identification of Verbal Multiword Expressions Carlos Ramisch", |
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| "raw_text": "Sunday, December 13, 2020 (continued) 17:20-18:00 Session 5: Shared Task Edition 1.2 of the PARSEME Shared Task on Semi-supervised Identification of Ver- bal Multiword Expressions Carlos Ramisch, Agata Savary, Bruno Guillaume, Jakub Waszczuk, Marie Candito, Ashwini Vaidya, Verginica Barbu Mititelu, Archna Bhatia, Uxoa I\u00f1urrieta, Voula Giouli, Tunga Gungor, Menghan Jiang, Timm Lichte, Chaya Liebeskind, Johanna Monti, Renata Ramisch, Sara Stymne, Abigail Walsh and Hongzhi Xu HMSid and HMSid2 at PARSEME Shared Task 2020: Computational Corpus Lin- guistics and unseen-in-training MWEs Jean-Pierre Colson Seen2Unseen at PARSEME Shared Task 2020: All Roads do not Lead to Unseen Verb-Noun VMWEs Caroline Pasquer, Agata Savary, Carlos Ramisch and Jean-Yves Antoine ERMI at PARSEME Shared Task 2020: Embedding-Rich Multiword Expression Identification Zeynep Yirmibe\u015foglu and Tunga Gungor TRAVIS at PARSEME Shared Task 2020: How good is (m)BERT at seeing the un- seen? Murathan Kurfal\u0131 MTLB-STRUCT @Parseme 2020: Capturing Unseen Multiword Expressions Using Multi-task Learning and Pre-trained Masked Language Models Shiva Taslimipoor, Sara Bahaadini and Ekaterina Kochmar MultiVitaminBooster and MultiVitaminRegressor at PARSEME Shared Task 2020: Combining Window-and Dependency-Based Features with Multilingual Contextu- alized Word Embeddings for Detecting Verbal Multiword Expressions Sebastian Gombert and Sabine Bartsch 18:00-18:10 Break 18:10-19:10 Session 6: Section reporting, panel discussion", |
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| "text": "Multiword terms in specialized lexicons \u2022 Representing semantic properties of MWEs in lexicons \u2022 Paving the way towards encoding lexical idiosyncrasies in constructions 1 http://multiword.sourceforge.net/mwelex2020 2 http://alt.qcri.org/siglex/ 3 https://elex.is/ iii MWE-specific topics \u2022 Computationally-applicable theoretical work on MWEs and constructions in psycholinguistics, corpus linguistics and formal grammars \u2022 MWE and construction annotation in corpora and treebanks", |
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