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arXiv · 1806.03740

Unsupervised Disambiguation of Syncretism in Inflected Lexicons

Ryan Cotterell·Christo Kirov·Sabrina J. Mielke·Jason Eisner
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Abstract

Lexical ambiguity makes it difficult to compute various useful statistics of a corpus. A given word form might represent any of several morphological feature bundles. One can, however, use unsupervised learning (as in EM) to fit a model that probabilistically disambiguates word forms. We present such an approach, which employs a neural network to smoothly model a prior distribution over feature bundles (even rare ones). Although this basic model does not consider a token's context, that very property allows it to operate on a simple list of unigram type counts, partitioning each count among different analyses of that unigram. We discuss evaluation metrics for this novel task and report results on 5 languages.

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Ryan Cotterell, Christo Kirov, Sabrina J. Mielke, Jason Eisner. 2018-06-10. Unsupervised Disambiguation of Syncretism in Inflected Lexicons. https://arxiv.org/abs/1806.03740

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