Paper: Learning From Relevant Documents In Large Scale Routing Retrieval

ACL ID H94-1071
Title Learning From Relevant Documents In Large Scale Routing Retrieval
Venue Human Language Technologies
Session Main Conference
Year 1994
Authors

The normal practice of selecting relevant documents for training routing queries is to either use all relevants or the 'best n' of them after a (retrieval) ranking operation with respect to each query. Using all relevants can introduce noise and ambiguities in training because documents can be long with many irrelevant portions. Using only the 'best n' risks leaving out documents that do not resemble a query. Based on a method of segmenting documents into more uniform size subdocuments, a better approach is to use the top ranked subdocument of every relevant. An alternative selection strategy is based on document properties without ranking. We found experimentally that short relevant documents are the quality items for training. Beginning portions of longer relevants are also useful. Using...