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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

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Word sense disambiguation (WSD) is a computational linguistics task likely to benefit from the tradition of combining different knowledge sources in artificial in telligence research. An important step in the exploration of this hypothesis is to determine which linguistic knowledge sources are most useful and whether their combination leads to improved results. We present a sense tagger which uses several knowledge sources. Tested accuracy exceeds 94 % on our evaluation corpus. Our system attempts to disambiguate all content words in running text rather than limiting itself to treating a restricted vocabulary of words. It is argued that this approach is more likely to assist the creation of practical systems. 1.

... we show in this paper some exemplary techniques in the ontology learning cycle that we have implemented in our ontology learning environment, KAON Text-To-Onto.

This paper describes two experiments: one exploring the amount of information relevant to sense disambiguation contained in the part-of-speech field of entries in a Machine Readable Dictionary (MRD); the other, more practical, experiment attempts sense disambiguation of all content words in a text assigning MRD homographs as sense tags using only partof -speech information. We have implemented a simple sense tagger which successfully tags 94% of words using this method. A plan to extend this work and implement an improved sense tagger is included. Contents 1 Introduction 1 2 Work so far 2 3 Experiments using part-of-speech 4 3.1 The Structure of a Lexicon: A Gedankenexperiment 5 3.2 Using a Tagger: A Practical Experiment 7 4 Conclusion 8 5 Further work 10 References 11 1 Introduction Sense tagging is the process of assigning the appropriate sense from a lexicon to each word token in a text 1 , similar to the way a grammatical category is assigned in part1 This is often loosen...

Abstract- Though the utility of domain Ontologies is now widely acknowledged in an increasing number of domains, several barriers must be overcome before Ontologies become practical and useful tools. A critical issue is the task of identifying, defining, and entering the concept definitions. In case of large and complex application domains this task can be lengthy, costly, and controversial (since different persons may have different points of view about the same concept). To reduce time, cost (and, sometimes, harsh discussions) it is highly advisable to refer, in constructing or updating an ontology, to the documents available in the field. In this paper we describe OntoLearn, a text-mining tool devised to improve human productivity during the process of ontology construction. 1.

We first reconsider the role of lexicographers in word-sense disambiguation as a computational task, as providers of both legacy material (dictionaries) and special test material for competitions like SENSEVAL. We suggest that the standard fine-grained division of senses and (larger) homographs by a lexicographer for use by a human reader may not be an appropriate goal for the computational WSD task. We argue that the level of sense-discrimination that NLP needs corresponds roughly to homographs, though we discuss psycholinguistic evidence that there are broad sense divisions with some etymological derivation (i.e. non-homographic) that are as distinct for humans as homographic ones and they may be part of the broad class of sensedivisions we seek to identify here. Fifteen years or more of WSD research has shown that it is this kind of discrimination that existing WSD programs are able to capture at the ~95% success level, whereas the full lexicographicallyderived division of senses seems to remain too hard for both programs and human discriminators. We link this discussion to the observation that major NLP tasks like MT and IR seem not to need independent WSD modules of the sort produced in the research field, even though they are undoubtedly doing WSD by other means. Our conclusion is that WSD should continue to focus on these broad discriminations, at which it can do very well, thereby possibly offering the close-to-100% success that IR needs (especially search-engine, rather than classic long-query) IR, and assume that this is what most NLP requires, with the possible exception of very fine questions of target word choice in MT. This proposal can be seen as reorienting WSD to what it can actually perform at the standard success levels, but we argue that this, rather...

There are now many computer programs for automatically determining the sense in which a word is being used. One would like to be able to say which are better, which worse, and also which words, or varieties of language, present particular problems to which algorithms. An evaluation exercise is required, and such an exercise requires a `gold standard' dataset of correct answers. Producing this proves to be a difficult and challenging task. In this paper I discuss the background, challenges and strategies, and present a detailed methodology for ensuring that the gold standard is not fool's gold. 1 Introduction There are now many computer programs for automatically determining the sense in which a word is being used. One would like to be able to say which are better, which worse, and also which words, or varieties of language, present particular problems to which algorithms. An evaluation exercise is required. A pilot (`SENSEVAL') is taking place under the auspices of ACL SIGLEX (the Le...