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Spell Once, Summon Anywhere: A Two-Level Open-Vocabulary Language Model ...
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Unsupervised Disambiguation of Syncretism in Inflected Lexicons ...
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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. ... : Published at NAACL 2018 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1806.03740 https://dx.doi.org/10.48550/arxiv.1806.03740
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