Unsupervised discovery of morphologically related words based on orthographic and semantic similarity

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dc.creatorBaroni, Marco
dc.creatorMatiasek, Johannes
dc.creatorTrost, Harald
dc.date2002-05-08
dc.date.accessioned2026-07-07T03:18:23Z
dc.date.available2026-07-07T03:18:23Z
dc.descriptionWe present an algorithm that takes an unannotated corpus as its input, and returns a ranked list of probable morphologically related pairs as its output. The algorithm tries to discover morphologically related pairs by looking for pairs that are both orthographically and semantically similar, where orthographic similarity is measured in terms of minimum edit distance, and semantic similarity is measured in terms of mutual information. The procedure does not rely on a morpheme concatenation model, nor on distributional properties of word substrings (such as affix frequency). Experiments with German and English input give encouraging results, both in terms of precision (proportion of good pairs found at various cutoff points of the ranked list), and in terms of a qualitative analysis of the types of morphological patterns discovered by the algorithm.
dc.description10 pages, to appear in the Proceedings of the Morphology/Phonology Learning Workshop of ACL-02
dc.identifierhttps://arxiv.org/abs/cs/0205006
dc.identifierhttp://arxiv.org/abs/cs/0205006
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31090
dc.subjectComputation and Language
dc.subjectI.2.7
dc.titleUnsupervised discovery of morphologically related words based on orthographic and semantic similarity
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