Intrinsic and Extrinsic Evaluations of Word Embeddings
DOI:
https://doi.org/10.1609/aaai.v30i1.9959Keywords:
embeddings, clusteringAbstract
In this paper, we first analyze the semantic composition of word embeddings by cross-referencing their clusters with the manual lexical database, WordNet. We then evaluate a variety of word embedding approaches by comparing their contributions to two NLP tasks. Our experiments show that the word embedding clusters give high correlations to the synonym and hyponym sets in WordNet, and give 0.88% and 0.17% absolute improvements in accuracy to named entity recognition and part-of-speech tagging, respectively.
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Published
2016-03-05
How to Cite
Zhai, M., Tan, J., & Choi, J. (2016). Intrinsic and Extrinsic Evaluations of Word Embeddings. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.9959
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Student Abstracts and Posters