An empirical approach to Lexical Tuning

NLP systems crucially depend on the knowledge structures devoted to describing and representing word senses. Although automatic Word Sense Disambiguation (WSD) is now an established task within empirically-based computational approaches to NLP, the suitability of the available set (and granularity) of senses is still a problem. Application domains exhibit speci c behaviors that cannot be fully predicted in advance. Suitable adaptation mechanisms have to be made available to NLP systems to tune existing large scale sense repositories to the practical needs of the target application, such as information extraction or machine translation. In this paper we describe a model of "lexical tuning" {the systematic adaptation of a lexicon to a corpus|that specializes the set of verb senses required for an NLP application, and builds inductively the corresponding lexical descriptions for those senses. 1 Word Sense Disambiguation and Lexical Tuning It is a commonplace observation (and the basis of much research e.g. [Rilo and Lehnert1993] that lexicons must be tuned or adapted to new domain corpora. This aspect, now often called Lexical Tuning, can take a number of forms, including: (a) adding a new sense to the lexical entry for a word (b) adding an entry for a word not already in the lexicon (c) adding a subcategorization or preference pattern etc. to any existing sense entry The system we describe is an original architecture for the overall task of corpus-based lexical tuning. This task is of general theoretical interest, but one that it is di cult to test directly, as a distinct NLP task, largely because of the di culty of incorporating the phenomenon into the standard markup-modeland-test paradigm of current empirical linguistics. A central issue in any application of empirical methods to computational linguistics is the evaluation procedure used, which is normally taken to consist in some form of experiment using premarked-up text divided into training and (unseen) test portions. Apart from the wellknown problem of the di erence between sense-sets in di erent lexicons, there are problems concerned with subjects having di culty assigning a word occurrence to one and only one sense during this markup phase. Kilgarri [Kilgarri 1993] has described such problems, though his gures suggest the di culties are probably not as serious as he claims [Wilks1997]. However,we have to ask what it means to evaluate the process of Lexical Tuning: this seems to require annotating in advance a new sense in a corpus that does not occur in the reference lexicon, when developing gold standard data for testing basic WSD. The clear answer is that, on the description given above, the sense extension (task (a) above: tuning to a new sense) CANNOT be pre-tagged and so no success rate for WSD can possibly exceed 100% MINUS the percentage of extended sense occurrences. One issue about lexical tuning that is not often discussed is: what the percentage of senses needing tuning IS in normal text? One anecdotal fact sometimes used is that, in any randomly chosen newspaper paragraph, each sentence will be likely to have an extended sense of at least one word, usually a verb, in the sense of a use that breaks conventional preferences and which might be considered extended or metaphorical use, and quite likely not in a standard lexicon. This is a claim that can be easily tested by anyone with a newspaper and a standard dictionary. The assumption under test in our project is that lexical tuning will assist the adaptation of a NLP task (e.g. Information Extraction) to a new domain and can therefore best be tested indirectly by its augmentation of the target system performances. There is already substantial evidence that some form of word sense disambiguation (WSD) assists any NL task, when applied as a separate module, and lexical tuning can be seen as a more advanced form of WSD. A second assumption not addressed in literature is that a tuned lexicon can signi cantly help the task of automatic pattern acquisition for template lling in an IE system. Currently, this task is largely performed by hand, with the help of more or less sophisticated interfaces [R. Yangarber1997]. The key idea adopted here is that an established initial lexicon can be tuned or adapted for verb senses in a given application domain. First verb occurrences in a corpus are distributed over a classi er that clusters their subcategorization patterns. This distribution allows a judgment of when a new pattern in the corpus, and not in the initial lexicon, should be assigned to an existing sense of the target dictionary, or established as a new sense to be added to it. 2 A general architecture for lexical tuning The proposed Lexical Tuning system rst processes a corpus with a tagger [Brill1992] and shallow parser 1. The structures so derived (essentially the subcategorization patterns (hereafter subcat) of individual verb occurrences in the corpus) are the distributed over a lattice structure, called a Galois Lattice (hereafter RGL), by an inductive method described in [Basili et al.1997], and brie y summarized in the next section. This is a device by which each occurring set of syntactic properties, for a given verb, is assigned to one node on the lattice, in an appropriate position for partial orderings with respect to all other subcat distributions. It is thus a sorting frame, with set inclusion relations, for the contexts of each appearance of the verb in the corpus.