Word sense disambiguation has developed as a sub-area of natural language processing, as if, like parsing, it was a well-defined task which was a pre-requisite to a wide range of language-understanding applications. First, I review earlier work which shows that a set of senses for a word is only ever defined relative to a particular human purpose, and that a view of word senses as part of the linguistic furniture lacks theoretical underpinnings. Then, I investigate whether and how word sense ambiguity is in fact a problem for NLP applications. There is now a substantial literature on the problem of word sense disambiguation (WSD). The goal of WSD research is generally taken to be disambiguation between the senses given in a dictionary, thesaurus or similar. The idea is simple enough and could be stated as follows: Many words have more than one meaning. When a person understands a sentence with an ambiguous word in it, that understanding is built on the basis of just one of the meanings. So, as some part of the human language understanding process, the appropriate meaning has been chosen from the range of possibilities. Stated in this way, it would seem that WSD might be a well-defined task, undertaken by a particular module within the human language processor. This module could then be modelled computationally in a WSD program, and this program, performing, as it did, one of the essential functions of the human language processor, would stand alongside a parser as a crucial component of a broad range of NLP applications. There are problems with this view. The simplest stems from the observation that different dictionaries * Research supported by the EPSRC, grant K18931. Email: Adam.Kilgarriff@itri.bton.ac.uk very often give different sets of senses for a word. A closer investigation reveals a lack of theoretical foundations to the concept of 'word sense'. The concept is intimately connected to our knowledge and experience of dictionaries, but these are social artifacts created to satisfy such human purposes as playing word-games, resolving family arguments, and making profits for publishers. Amid all these competing goals, the pursuit of truth is not always dominant. In particular, a standard dictionary specifies the range of meaning of a word in a list, possibly nested, of senses. This is not the outcome of an analysis of how word-meaning operates, but is, rather, a response to constraints imposed by: • tradition • the printed page The format of the dictionary has remained fairly stable since Dr. Johnson's day. The reasons for the format, and the reasons it has proved so resistant to change and innovation, are explored at length in Nunberg (1994) . In short, the development of printed discourse, particularly the new periodicals, in England in the early part of the eighteenth century brought about a re-evaluation of the nature of meaning. No longer could it be assumed that a disagreement or confusion about a word's meaning could be settled face-to-face, and it seemed at the time that the new discourse would only be secure if there was some mutually acceptable authority on what words meant. The resolution to the crisis came in the form of Johnson's Dictionary. Thus, from its inception, the modern dictionary has had a crucial symbolic role
SENSEVAL defined the first evaluation framework for word sense disambiguation (WSD) in NLP REF .
What is word sense disambiguation good for?
Proc. Natural Language Processing Pacific Rim Symposium. Phuket, Thailand. December 1997. Pp 209--214.
Computer Science