文章基本信息

标题：From Segmentation to Analyses: a Probabilistic Model for Unsupervised Morphology Induction
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作者：Toms Bergmanis ; Sharon Goldwater
期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2017
卷号：2017
页码：337-346
语种：English
出版社：ACL Anthology
摘要：A major motivation for unsupervised morphological analysis is to reduce the sparse data problem in under-resourced languages. Most previous work focus on segmenting surface forms into their constituent morphs (taking: tak +ing), but surface form segmentation does not solve the sparse data problem as the analyses of take and taking are not connected to each other. We present a system that adapts the MorphoChains system (Narasimhan et al., 2015) to provide morphological analyses that aim to abstract over spelling differences in functionally similar morphs. This results in analyses that are not compelled to use all the orthographic material of a word (stopping: stop +ing) or limited to only that material (acidified: acid +ify +ed). On average across six typologically varied languages our system has a similar or better F-score on EMMA (a measure of underlying morpheme accuracy) than three strong baselines; moreover, the total number of distinct morphemes identified by our system is on average 12.8% lower than for Morfessor (Virpioja et al., 2013), a state-of-the-art surface segmentation system.