Morphology Induction from Term Clusters

Proceedings of the 9th Conference on Computational Natural Language Learning (CoNLL), pages 128–135, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics Morphology Induction From Term Clusters Dayne Freitag HNC Software, LLC 3661 Valley Centre Drive San Diego, CA 92130, USA daynefreitag@fairisaac.com Abstract We address the problem of learning a morphological automaton directly from a monolingual text corpus without re- course to additional resources. Like pre- vious work in this area, our approach ex- ploits orthographic regularities in a search for possible morphological segmentation points. Instead of affixes, however, we search for affix transformation rules that express correspondences between term clusters induced from the data. This focuses the system on substrings hav- ing syntactic function, and yields cluster- to-cluster transformation rules which en- able the system to process unknown mor- phological forms of known words accu- rately. A stem-weighting algorithm based on Hubs and Authorities is used to clar- ify ambiguous segmentation points. We evaluate our approach using the CELEX database. 1 Introduction This paper presents a completely unsupervised method for inducing morphological knowledge di- rectly from a large monolingual text corpus. This method works by searching for transformation rules that express correspondences between term clusters which are induced from the corpus in an initial step. It covers both inflectional and derivational morphol- ogy, and is able to process previously unseen morphs of a word, as long as one of its morphs has been as- signed to a cluster. Aside from its academic appeal, acquisition of this morphological knowledge is a step toward the goal of rapidly retargetable natural language pro- cessing. Toward this end, we envisage two uses for it: 1. It can be used to perform morphological nor- malization (i.e., stemming (Porter, 1980)). 2. In the form of transformation rules, it can help us classify unknown words, thereby enhancing the utility of cluster-based features for applica- tions such as information extraction (Miller et al., 2004; Freitag, 2004). There is a considerable literature on the problem of morphology induction in general, and unsuper- vised (or lightly supervised) induction in particular. Much of the work attempts to exploit orthographic regularities alone, seeking affixation patterns (or sig- natures) that permit a compressive representation of the corpus. Several researchers propose algorithms based on the minimum description length (MDL) principle, achieving reasonable success in discov- ering regular morphological patterns (Brent et al., 1995; Goldsmith, 2000; Creutz and Lagus, 2002; Argamon et al., 2004). MDL has information the- oretic underpinnings, and an information theoretic objective function achieves similar success (Snover et al., 2002). Note that none of these approaches at- tempts to account for the syntactic dimension of af- fixation. And all must adopt strategies to cope with a very large search space (the power set of the vocab- 128 ulary, in the limit). Such strategies form a common theme in these papers. Our approach implicitly employs term co- occurrence statistics in the form of statistically de- rived term clusters. A number of researchers use such statistics directly. A common technique is to cast …

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