Identifying Terms by their Family and Friends

Multi-word terms are traditionally identified using statistical techniques or, more recently, using hybrid techniques combining statistics with shallow linguistic information. Al)proaches to word sense disambiguation and machine translation have taken advantage of contextual information in a more meaningflfl way, but terminology has rarely followed suit. We present an approach to term recognition which identifies salient parts of the context and measures their strength of association to relevant candidate terms. The resulting list of ranked terms is shown to improve on that produced by traditional methods, in terms of precision and distribution, while the information acquired in the process can also be used for a variety of other applications, such as disambiguation, lexical tuning and term clustering.

• Type: Conference
• Year: 2000
• Where: COLING
• Authors: Diana Maynard, Sophia Ananiadou
• Keywords: COLING 2000, COLING 2008, Multi-word Terms, Techniques Combining Statistics, Word Sense Disambiguation