In this paper, we present a novel approach to partial parsing that produces dependency links between words of a sentence. The partial parser called a lightweight dependency analyzer uses information encoded in supertags and hence can produce constituency-based as well as dependency-based analyses. The lightweight dependency analyzer has been used for text chunking, including noun and verb group chunking. We also present a proposal for a general framework for parser evaluation that is applicable for evaluating both constituency-based and dependency-based, partial and complete parsers. The performance results of the lightweight dependency analyzer on Wall Street Journal and Brown corpus using the proposed evaluation metrics are discussed.

An algorithmic approach to the semantic interpretation of deverbal nominalizations found in encyclopedic texts, such as support, publication and control, is described. Interpreting these nominalizations is crucial because they are quite common in encyclopedic texts, hence a great deal of information is represented within them. Interpretation involves three tasks: deciding whether the nominalization is being used in a verbal or non-verbal sense; disambiguating the nominalized verb when a verbal sense is used; and determining the fillers of the thematic roles of the verbal concept or predicate of the nominalization. A verbal sense can be recognized by the presence of modifiers that represent the arguments of the verbal concept. It is these same modifiers which provide the semantic clues to disambiguate the nominalized verb. In the absence of explicit modifiers, heuristics are used to discriminate between verbal and non-verbal senses. A correspondence between verbs and their nominalizations is exploited so that only a small amount of additional knowledge is needed to handle the nominal form. These methods are tested in the domain of encyclopedic texts and the results are shown.

In this paper, we propose a new ambiguity representation scheme; Structure Preference Relation (SPR), which consists of useful quantitative distribution information for ambiguous structures. Two automatic acquisition algorithms, the first acquired from a treebank, and the second acquired from raw texts, are introduced, and some experimental results which prove the availability of the algorithms are also given. Finally, we introduce some SPR applications in linguistics and natural language processing, such as preference-based parsing and the discovery of representative ambiguous structures, and propose some future research directions.

Prepositional Phrases (PP) perform a variety of syntactic functions in a conventional sentence, and cause severe problems to computer systems that automatically analyse the sentential syntax. A major issue in this area has been the automatic determination of the syntactic functions of PPs. Most work published so far makes use of the probabilistic approach, and attach PPs to either the antecedent noun or verb phrase. Due to the natural limitation of the probabilistic approach, it is important to evaluate the linguistic behaviour of prepositional phrases and propose qualitative solutions to the problem. In this article, I first provide a detailed account of statistics regarding the frequency of use for (i) types of prepositions, (ii) syntactic categories as realisations of prepositional complements, and (iii) the syntactic functions of prepositional phrases. Statistics reported here all derive from a representative corpus of contemporary British English. I then describe a set of rules that has been implemented in order to label PPs automatically for their syntactic functions. I finally report on the coverage of these rules empirically observed in an experiment which involved a set of naturally occurring PPs as test data.