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Evaluating learning language representations
KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS. Gavagai, Sweden.ORCID iD: 0000-0003-4042-4919
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2015 (English)Conference paper (Refereed)Text
Abstract [en]

Machine learning offers significant benefits for systems that process and understand natural language: (a) lower maintenance and upkeep costs than when using manually-constructed resources, (b) easier portability to new domains, tasks, or languages, and (c) robust and timely adaptation to situation-specific settings. However, the behaviour of an adaptive system is less predictable than when using an edited, stable resource, which makes quality control a continuous issue. This paper proposes an evaluation benchmark for measuring the quality, coverage, and stability of a natural language system as it learns word meaning. Inspired by existing tests for human vocabulary learning, we outline measures for the quality of semantic word representations, such as when learning word embeddings or other distributed representations. These measures highlight differences between the types of underlying learning processes as systems ingest progressively more data.

Place, publisher, year, edition, pages
Springer, 2015. 254-260 p.
Lecture Notes in Computer Science, ISSN 0302-9743
Keyword [en]
Evaluation, Language representations, Machine learning, Semantic spaces, Word embeddings, Adaptive control systems, Artificial intelligence, Association reactions, Benchmarking, C (programming language), Computational linguistics, Learning systems, Natural language processing systems, Semantics, Distributed representation, Embeddings, Natural language systems, Semantic Space, Vocabulary learning, Word representations, Quality control
National Category
Language Technology (Computational Linguistics)
URN: urn:nbn:se:kth:diva-181546DOI: 10.1007/978-3-319-24027-5_25ISI: 000364677800028ScopusID: 2-s2.0-84945979173ISBN: 9783319240268OAI: diva2:910588
6th International Conference on Labs of the Evaluation Forum, CLEF 2015; Toulouse; France; 8-11 September 2015
European Science Foundation (ESF)

QC 20160309

Available from: 2016-03-09 Created: 2016-02-02 Last updated: 2016-04-13Bibliographically approved

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Karlgren, Jussi
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Theoretical Computer Science, TCS
Language Technology (Computational Linguistics)

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