Using Local Alignments for Relation Recognition

This paper discusses the problem of marrying structural similarity with semantic relatedness for Information Extraction from text. Aiming at accurate recognition of relations, we introduce local alignment kernels and explore various possibilities of using them for this task. We give a definition of...

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Bibliographic Details
Published in:The Journal of artificial intelligence research 2010-01, Vol.38, p.1-48
Main Authors: Katrenko, S., Adriaans, P. W., Van Someren, M.
Format: Article
Language:English
Subjects:
Alignment
Artificial intelligence
Cognitive tasks
Information retrieval
Kernels
Recognition
Semantics
Sequences
Similarity
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Summary:This paper discusses the problem of marrying structural similarity with semantic relatedness for Information Extraction from text. Aiming at accurate recognition of relations, we introduce local alignment kernels and explore various possibilities of using them for this task. We give a definition of a local alignment (LA) kernel based on the Smith-Waterman score as a sequence similarity measure and proceed with a range of possibilities for computing similarity between elements of sequences. We show how distributional similarity measures obtained from unlabeled data can be incorporated into the learning task as semantic knowledge. Our experiments suggest that the LA kernel yields promising results on various biomedical corpora outperforming two baselines by a large margin. Additional series of experiments have been conducted on the data sets of seven general relation types, where the performance of the LA kernel is comparable to the current state-of-the-art results.
ISSN:1076-9757
1076-9757
1943-5037
DOI:10.1613/jair.2964