Lin, Dekang and Patrick Pantel. 2001. Induction of semantic classes from natural language text. In SIGKDD-01, San Francisco.  Home/Search Document Details and Download
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Discovering Word Senses from Text - Pantel, Lin (2002) (10 citations) Self-citation (Lin Pantel) (Correct)
....the centroids of the clusters as the initial K centroids of K means clustering. The sample size counterbalances the quadratic running time of average link to make Buckshot efficient: O(KxTxn nlogn) The parameters K and T are usually considered to be small numbers. CBC is a descendent of UNICON [13], which also uses small and tight clusters to construct initial centroids. We compare them in Section 4.4 after presenting the CBC algorithm. 3. WORD SIMILARITY Following [12] we represent each word by a feature vector. Each feature corresponds to a context in which the word occurs. For ....
....to the algorithm for discovering senses is that once an element e is assigned to a cluster c, the intersecting features between e and c are removed from e. This allows CBC to discover the less frequent senses of a word and to avoid discovering duplicate senses. 4. 4 Comparison with UNICON UNICON [13] also constructs cluster centroids using a small set of similar elements, like the committees in CBC. One of the main differences between UNICON and CBC is that UNICON only guarantees that the committees do not have overlapping members. However, the centroids of two committees may still be quite ....
Lin, D. and Pantel, P. 2001. Induction of semantic classes from natural language text. In Proceedings ofSIGKDD-01. pp. 317 322. San Francisco, CA.
Discovering Word Senses from Text - Pantel, Lin (2002) (10 citations) Self-citation (Lin Pantel) (Correct)
....the centroids of the clusters as the initial K centroids of K means clustering. The sample size counterbalances the quadratic running time of average link to make Buckshot efficient: O(K#T#n nlogn) The parameters K and T are usually considered to be small numbers. CBC is a descendent of UNICON [13], which also uses small and tight clusters to construct initial centroids. We compare them in Section 4.4 after presenting the CBC algorithm. 3. WORD SIMILARITY Following [12] we represent each word by a feature vector. Each feature corresponds to a context in which the word occurs. For ....
....II using the same input except replacing E with R. Output: a list of committees. Figure 1. Phase II of CBC. between e and c are removed from e. This allows CBC to discover the less frequent senses of a word and to avoid discovering duplicate senses. 4. 4 Comparison with UNICON UNICON [13] also constructs cluster centroids using a small set of similar elements, like the committees in CBC. One of the main differences between UNICON and CBC is that UNICON only guarantees that the committees do not have overlapping members. However, the centroids of two committees may still be quite ....
Lin, D. and Pantel, P. 2001. Induction of semantic classes from natural language text. In Proceedings of SIGKDD-01. pp. 317322. San Francisco, CA.
Clustering Syntactic Positions with Similar Semantic.. - Gamallo, Agustini, Lopes (Correct)
No context found.
Lin, Dekang and Patrick Pantel. 2001. Induction of semantic classes from natural language text. In SIGKDD-01, San Francisco.
Clustering Syntactic Positions with Similar Semantic.. - Gamallo, Agustini, Lopes (Correct)
No context found.
Lin, Dekang and Patrick Pantel. 2001. Induction of semantic classes from natural language text. In SIGKDD-01, San Francisco.
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