Learning Morpho-Lexical Probabilities from an Untagged Corpus with an Application to Hebrew

This paper proposes a new approach for acquiring morpho-lexical probabilities from an untagged corpus. This approach demonstrates a way to extract very useful and nontrivial information from an untagged corpus, which otherwise would require laborious tagging of large corpora. The paper describes the use of these morpho-lexical probabilities as an information source for morphological disambiguation in Hebrew. The suggested method depends primarily on the following property: a lexical entry in Hebrew may have many different word forms, some of which are ambiguous and some of which are not. Thus, the disambiguation of a given word can be achieved using other word forms of the same lexical entry. Even though it was originally devised and implemented for • dealing with the morphological ambiguity problem in Hebrew, the basic idea can be extended and used to handle similar problems in other languages with rich morphology.