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Games on multiword expressions for community building
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In: ISSN: 1450-9687 ; EISSN: 2217-9461 ; INFOtheca : Journal of Information and Library Science ; https://hal.archives-ouvertes.fr/hal-01635502 ; INFOtheca : Journal of Information and Library Science, Serbian Academic Library Association, 2017, 17 (2) (2017)
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Expressions polylexicales verbales : étude de la variabilité en corpus
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In: TALN-RECITAL 2017 ; https://hal.archives-ouvertes.fr/hal-01637355 ; TALN-RECITAL 2017, Jun 2017, Orléans, France (2017)
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The PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions
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In: MWE 2017 - Proceedings of the 13th Workshop on Multiword Expressions ; https://hal.archives-ouvertes.fr/hal-01504624 ; MWE 2017 - Proceedings of the 13th Workshop on Multiword Expressions, Apr 2017, Valencia, Spain. pp.31 - 47 (2017)
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Projecting Multiword Expression Resources on a Polish Treebank
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In: Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing ; 6th Workshop on Balto-Slavic Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-01504629 ; 6th Workshop on Balto-Slavic Natural Language Processing, Apr 2017, Valence, Spain. pp.20 - 26 (2017)
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Annotated corpora and tools of the PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions (edition 1.0)
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Crowdsourcing lexical semantic judgements from bilingual dictionary users
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Abstract:
© 2017 Dr. Richard James Fothergill ; Words can take on many meanings, and collecting and identifying example usages representative of the full variety of meanings words can take is a bottleneck to the study of lexical semantics using statistical approaches. To perform supervised word sense disambiguation (WSD), or to evaluate knowledge-based methods, a corpus of texts annotated with senses from a dictionary may be constructed by paid experts. However, the cost usually prohibits more than a small sample of words and senses being represented in the corpus. Crowdsourcing methods promise to acquire data more cheaply, albeit with a greater challenge for quality control. Most crowdsourcing to date has incentivised participation in the form of a payment or by gamification of the resource construction task. However, with paid crowdsourcing the cost of human labour scales linearly with the output size, and while game playing volunteers may be free, gamification studies must compete with a multi-billion dollar games industry for players. In this thesis we develop and evaluate resources for computational semantics, working towards a crowdsourcing method that extracts information from naturally occurring human activities. A number of software products exist for glossing Japanese text with entries from a dictionary for English speaking students. However, the most popular ones have a tendency to either present an overwhelming amount of information containing every sense of every word or else hide too much information and risk removing senses with particular relevance to a specific text. By offering a glossing application with interactive features for exploring word senses, we create an opportunity to crowdsource human judgements about word senses and record human interaction with semantic NLP.
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Keyword:
computational lexical semantics; crowdsourcing; glossing; Japanese-English dictionary; lexical segmentation; multiword expressions; MWE disambiguation; MWEs; natural language processing; tokenisation; word sense disambiguation; WSD
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URL: http://hdl.handle.net/11343/194282
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Parsing and MWE Detection: Fips at the PARSEME Shared Task
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In: Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017) pp. 54-59 (2017)
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Remembering you read “doctoral dissertation”: Phrase frequency effects in recall and recognition memory
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Multi-word Adverbs – How well are they handled in Parsing and Machine Translation?
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In: Volk, Martin; Graën, Johannes (2017). Multi-word Adverbs – How well are they handled in Parsing and Machine Translation? In: The 3rd Workshop on Multi-word Units in Machine Translation and Translation Technology (MUMTTT 2017), London, 14 November 2017 - 14 November 2017. (2017)
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