Novelty Detection Based on Sentence Level Patterns

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Novelty Detection Based on Sentence Level Patterns (2005)

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by Xiaoyan Li

Venue:	CIKM
Citations:	24 - 0 self

BibTeX

@INPROCEEDINGS{Li05noveltydetection,
    author = {Xiaoyan Li},
    title = {Novelty Detection Based on Sentence Level Patterns},
    booktitle = {CIKM},
    year = {2005},
    pages = {744--751}
}

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Abstract

The detection of new information in a document stream is an important component of many potential applications. In this paper, a new novelty detection approach based on the identification of sentence level patterns is proposed. Given a user’s information need, some patterns in sentences such as combinations of query words, named entities and phrases, may contain more important and relevant information than single words. Therefore, the proposed novelty detection approach focuses on the identification of previously unseen query-related patterns in sentences. Specifically, a query is preprocessed and represented with patterns that include both query words and required answer types. These patterns are used to retrieve sentences, which are then determined to be novel if it is likely that a new answer is present. An analysis of patterns in sentences was performed with data from the TREC 2002 novelty track and experiments on novelty detection were carried out on data from the TREC 2003 and 2004 novelty tracks. The experimental results show that the proposed pattern-based approach significantly outperforms all three baselines in terms of precision at top ranks.

Keyphrases

sentence level pattern novelty detection novelty track query word user information need novelty detection approach many potential application top rank new information document stream single word important component pattern-based approach answer type unseen query-related pattern relevant information new novelty detection approach experimental result new answer

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