Extracting semantic clusters from the alignment of definitions

• Authors: Gerardo Sierra;John McNaught
• Venue: COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
• Year: 2000

Through the alignment of definitions from two or more different sources, it is possible to retrieve pairs of words that can be used indistinguishably in the same sentence without changing the meaning of the concept. As lexicographic work exploits common defining schemes, such as genus and differentia, a concept is similarly defined by different dictionaries. The difference in words used between two lexicographic sources lets us extend the lexical knowledge base, so that clustering is available through merging two or more dictionaries into a single database and then using an appropriate alignment technique. Since alignment starts from the same entry of two dictionaries, clustering is faster than any other technique.The algorithm introduced here is analogy-based, and starts from calculating the Levenshtein distance, which is a variation of the edit distance, and allows us to align the definitions. As a measure of similarity, the concept of longest collocation couple is introduced, which is the basis of clustering similar words. The process iterates, replacing similar pairs of words in the definitions until no new clusters are found.