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1
Semantic relations ate the Machine learning era: where are the (good old) patterns gone?
In: WS2: Information Retrieval in terminology Using Lexical Knowledge Patterns @ LSP2017 (Language for Specific Purposes) ; https://hal.archives-ouvertes.fr/hal-03119889 ; WS2: Information Retrieval in terminology Using Lexical Knowledge Patterns @ LSP2017 (Language for Specific Purposes), IITF (International Institute for Terminology Research), Jun 2017, Bergen, Norway (2017)
Abstract: Keynote talk ; International audience ; Although looking for semantic relations in text has been the topic of a large variety of research works since the early 90's, it is still considered as a difficult task without easy solution. This question is addressed by more and more researchers with very different backgrounds, either in computer science (NLP, semantic web, information extraction, etc.), linguistics, terminology, etc. After various attempts of cross-disciplinary fertilization, it seems that each domain investigates its own solutions and methods without a real integration of results from other disciplines. For instance, the large efforts carried out in terminology and in computational linguistics to evaluate, define, improve pattern-based approaches are not much taken into account in Natural Language Processing, and even less in ontology learning. In a symmetric manner, relation extraction using machine learning has still little impact in linguistic analyses and when designing lexical resources. And Hearst's patterns are regularly the core of new work that little advances the state of the art. I will first draw an overview of the current approaches to identify semantic relations in text, with a special focus on the ways pattern -based solutions have evolved since early works. Then I will present a few works that have evaluated the complementarity of various techniques to support this task. I will finally promote the idea to capitalize better all the experiments and tools developed up to now, in particular by sharing patterns and learning methods, but also but investigating more systematically how existing techniques can be used together in a single platform, and mutually benefit of each other's results.
Keyword: [INFO]Computer Science [cs]
URL: https://hal.archives-ouvertes.fr/hal-03119889
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2
“Multilingual Knowledge and Technology Transfer” Edited by: Gilles-Maurice de Schryver International Network
In: http://tshwanedje.com/publications/tama.pdf (2003)
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3
TermNet news : journal of the international cooperation in terminology ; joint publication of the International Network for Terminology (TermNet), the International Information Centre for Terminology (Infoterm), the Association for Terminology and Knowledge Transfer (GTW), the International Institute for Terminology Research (IITF) and the East Asia Forum on Terminology (EAFTerm) [<Journal>]
DNB Subject Category Language
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4European Association for Terminology (EAFT)
https://www.eaft-aet.net/en
Topic: Technical language
Source type: Bibliographies; Linguistic associations; Newsletters / Mailing lists
Access: partial free access

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