Knowledge-Free Induction of Inflectional Morphologies

Knowledge-Free Induction of Inflectional Morphologies Patrick SCHONE Daniel JURAFSKY University of Colorado at Boulder University of Colorado at Boulder Boulder, Colorado 80309 Boulder, Colorado 80309 schone@cs.colorado.edu jurafsky@cs.colorado.edu Abstract We propose an algorithm to automatically induce the morphology of inflectional languages using only text corpora and no human input. Our algorithm combines cues from orthography, semantics, and syntactic distributions to induce morphological relationships in German, Dutch, and English. Using CELEX as a gold standard for evaluation, we show our algorithm to be an improvement over any knowledge-free algorithm yet proposed. 1 Introduction Many NLP tasks, such as building machine-readable dictionaries, are dependent on the results of morphological analysis. While morphological analyzers have existed since the early 1960s, current algorithms require human labor to build rules for morphological structure. In an attempt to avoid this labor-intensive process, recent work has focused on machine-learning approaches to morphological structure using large corpora. induce In this paper, we propose a knowledge-free algorithm to automatically induce the morphology structures of a language. Our algorithm takes as input a large corpus and produces as output a set of conflation sets indicating the various inflected and derived forms for each word in the language. As an example, the conflation set of the word “abuse” would contain “abuse”, “abused”, “abuses”, “abusive”, “abusively”, and so forth. Our algorithm extends earlier approaches to morphology induction by combining various induced information sources: the semantic relatedness of the affixed forms using a Latent Semantic Analysis approach to corpus- based semantics (Schone and Jurafsky, 2000), affix frequency, syntactic context, and transitive closure. Using the hand-labeled CELEX lexicon (Baayen, et al., 1993) as our gold standard, the current version of our algorithm achieves an F-score of 88.1% on the task of identifying conflation sets in English, outperforming earlier algorithms. Our algorithm is also applied to German and Dutch and evaluated on its ability to find prefixes, suffixes, and circumfixes in these languages. To our knowledge, this serves as the first evaluation of complete regular morphological induction of German or Dutch (although researchers such as Nakisa and Hahn (1996) have evaluated induction algorithms on 2morphological sub-problems in German). Previous Approaches Previous morphology induction approaches have fallen into three categories. These categories differ depending on whether human input is provided and on whether the goal is to obtain affixes or complete morphological analysis. We here briefly describe work in each category. 2.1 Using a Knowledge Source to Bootstrap Some researchers begin with some initial human- labeled source from which they induce other morphological components. In particular, Xu and Croft (1998) use word context derived from a corpus to refine Porter stemmer output. Gaussier (1999) induces derivational morphology using an inflectional lexicon which includes part of speech information. Grabar and Zweigenbaum (1999) use the SNOMED corpus of semantically-arranged medical terms to find semantically-motivated morphological relationships. Also, Yarowsky and Wicentowski (2000) obtained outstanding results at inducing English past tense after beginning with a list of the open class roots in the language, a table of a language’s inflectional parts …

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