Abstract
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.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Baroni, M., Lenci, A.: How we BLESSed distributional semantic evaluation. In: Proceedings of the 2011 Workshop on GEometrical Models of Natural Language Semantics, pp. 1–10. ACL (2011)
Cook, P., Lau, J.H., McCarthy, D., Baldwin, T.: Novel word-sense identification. In: Proceedings of COLING, pp. 1624–1635 (2014)
Frishkoff, G.A., Collins-Thompson, K., Perfetti, C.A., Callan, J.: Measuring incremental changes in word knowledge: Experimental validation and implications for learning and assessment. Behavior Research Methods 40(4), 907–925 (2008)
Frishkoff, G.A., Perfetti, C.A., Collins-Thompson, K.: Predicting robust vocabulary growth from measures of incremental learning. Scientific Studies of Reading 15(1), 71–91 (2011)
Hill, F., Reichart, R., Korhonen, A.: Simlex-999: Evaluating semantic models with (genuine) similarity estimation (2014). arXiv preprint arXiv:1408.3456
Karlgren, J. (ed.): Proceedings of the EACL workshop on New Text: Wikis and blogs and other dynamic text sources, EACL 2006
Landauer, T., Dumais, S.: A solution to plato’s problem: The latent semantic analysis theory of acquisition, induction and representation of knowledge. Psychological Review 104(2), 211–240 (1997)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of NIPS, pp. 3111–3119 (2013)
Turney, P.D., Pantel, P.: From Frequency to Meaning: Vector Space Models of Semantics. Journal of Artificial Intelligence Research 37, 141–188 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Karlgren, J. et al. (2015). Evaluating Learning Language Representations. In: Mothe, J., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2015. Lecture Notes in Computer Science(), vol 9283. Springer, Cham. https://doi.org/10.1007/978-3-319-24027-5_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-24027-5_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24026-8
Online ISBN: 978-3-319-24027-5
eBook Packages: Computer ScienceComputer Science (R0)