Abstract not found

The automatic disambiguation of word senses has been an interest and concern since the earliest days of computer treatment of language in the 1950's. Sense disambiguation is an “intermediate task ” (Wilks and Stevenson, 1996) which is not an end in itself, but rather is necessary at one level or another to accomplish most natural language processing tasks. It is

As a lexical knowledge base constructed automatically from the definitions and example sentences in two machine-readable dictionaries (MRDs), MindNet embodies several features that distinguish it from prior work with MRDs. It is, however, more than this static resource alone. MindNet represents a general methodology for acquiring, structuring, accessing, and exploiting semantic information from natural language text. This paper provides an overview of the distinguishing characteristics of MindNet, the steps involved in its creation, and its extension beyond dictionary text. 1

Statistical language learning research takes the view that many traditional natural language processing tasks can be solved by training probabilistic models of language on a sufficient volume of training data. The design of statistical language learners therefore involves answering two questions: (i) Which of the multitude of possible language models will most accurately reflect the properties necessary to a given task? (ii) What will constitute a sufficient volume of training data? Regarding the first question, though a variety of successful models have been discovered, the space of possible designs remains largely unexplored. Regarding the second, exploration of the design space has so far proceeded without an adequate answer. The goal of this thesis is to advance the exploration of the statistical language learning design space. In pursuit of that goal, the thesis makes two main theoretical contributions: it identifies a new class of designs by providing a novel theory of statistical natural language processing, and it presents the foundations for a predictive theory of data requirements to assist in future design explorations. The first of these contributions is called the meaning distributions theory. This theory

... In this paper we argue for a linguistically principled approach to disambiguation, in which relevant contextual clues are narrowly defined, in syntactic and semantic terms, and in which only highly reliable clues are exploited. Statistical methods play a definite role in this work, helping to organize and analyze data, but the disambiguation method itself does not employ statistical data or decision criteria. This approach results in improved understanding of the disambiguation problem both in general and on a word-specific basis and leads to broadly applicable and nearly errorless clues to word sense. The approach is illustrated by an experiment discriminating among the senses of adjectives, which have been relatively neglected in work on sense disambiguation. In particular, the paper assesses the potential of nouns for discriminating among the senses of adjectives that modify them. This assessment is based on an empirical study of five of the most frequent ambiguous adjectives in English: hard, light, old, right, and short. About three-quarters of all instances of these adjectives can be disambiguated almost errorlessly by the nouns they modify or by the syntactic constructions in which they occur. Such disambiguation requires only simple rules, which can be automated easily. Furthermore, a small number of semantic attributes supply a compact means of representing the noun clues in a very few rules. Clues other than nouns are required when modified nouns are not useable. The sense of an ambiguous modified noun may be needed to determine the relevant semantic attribute for disambiguation of a target adjective; and other adjectives, verbs, and grammatical constructions all show evidence of high reliability, and sometimes of high applicability, when they stand in specific, ...

This paper describes an algorithm for automatically interpreting noun sequences in unrestricted text. This system uses broadcoverage semantic information which has been acquired automatically by analyzing the definitions in an on-line dictionary. Previously, computational studies of noun sequences made use of hand-coded semantic information, and they applied the analysis rules sequentially. In contrast, the task of analyzing noun sequences in unrestricted text strongly favors an algorithm according to which the rules are applied in parallel and the best interpretation is determined by weights associated with rule applications.

This paper describes a heuristic-based approach to word-sense disambiguation. The heuristics that are applied to disambiguate a word depend on its part of speech, and on its relationship to neighboring salient words in the text. Parts of speech are found through a tagger, and related neighboring words are identified by a phrase extractor operating on the tagged text. To suggest possible senses, each heuristic draws on semantic relations extracted from a Webster's dictionary and the semantic thesaurus WordNet. For a given word, all applicable heuristics are tried, and those senses that are rejected by all heuristics are discarded. In all, the disambiguator uses 39 heuristics based on 12 relationships.