Unsupervised Learning of Word-Category Guessing Rules

Unsupervised Learning of Word-Category Guessing Rules Andrei Mikheev HCRC Language Technology Group University of Edinburgh 2 Buccleuch Place Edinburgh EH8 9LW, Scotland, UK : Andrei.Mikheev~ed.ac.uk Abstract Words unknown to the lexicon present a substantial problem to part-of-speech tag- ging. In this paper we present a technique for fully unsupervised statistical acquisi- tion of rules which guess possible parts- of-speech for unknown words. Three com- plementary sets of word-guessing rules are induced from the lexicon and a raw cor- pus: prefix morphological rules, suffix mor- phological rules and ending-guessing rules. The learning was performed on the Brown Corpus data and rule-sets, with a highly competitive performance, were produced and compared with the state-of-the-art. 1 Introduction Words unknown to the lexicon present a substan- tial problem to part-of-speech (POS) tagging of real- world texts. Taggers assign a single POS-tag to a word-token, provided that it is known what parts- of-speech this word can take on in principle. So, first words are looked up in the lexicon. However, 3 to 5% of word tokens are usually missing in the lex- icon when tagging real-world texts. This is where word-Pos guessers take their place -- they employ the analysis of word features, e.g. word leading and trailing characters, to figure out its possible POS cat- egories. A set of rules which on the basis of ending characters of unknown words, assign them with sets of possible POS-tags is supplied with the Xerox tag- ger (Kupiec, 1992). A similar approach was taken in (Weischedel et al., 1993) where an unknown word was guessed given the probabilities for an unknown word to be of a particular POS, its capitalisation fea- ture and its ending. In (Brill, 1995) a system of rules which uses both ending-guessing and more morpho- logically motivated rules is described. The best of these methods are reported to achieve 82-85% of tagging accuracy on unknown words, e.g. (Brill, 1995; Weischedel et al., 1993). The major topic in the development of word-Pos guessers is the strategy which is to be used for the acquisition of the guessing rules. A rule-based tag- ger described in (Voutilainen, 1995) is equipped with a set of guessing rules which has been hand-crafted using knowledge of English morphology and intu- ition. A more appealing approach is an empiri- cal automatic acquisition of such rules using avail- able lexical resources. In (Zhang&Kim, 1990) a system for the automated learning of morphologi- cal word-formation rules is described. This system divides a string into three regions and from train- ing examples infers their correspondence to under- lying morphological features. Brill (Brill, 1995) out- lines a transformation-based learner which learns guessing rules from a pre-tagged training corpus. A statistical-based suffix learner is presented in (Schmid, 1994). From a pre-tagged training cor- pus it constructs the suffix tree where every suf- fix is associated with its information measure. Al- though the learning process in these and some other systems is fully unsupervised and the accuracy of obtained rules reaches current state-of-the-art, they require specially …

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