Conference Paper

Keyphrase extraction-based query expansion in digital libraries

DOI: 10.1145/1141753.1141800 Conference: ACM/IEEE Joint Conference on Digital Libraries, JCDL 2006, Chapel Hill, NC, USA, June 11-15, 2006, Proceedings
Source: DBLP


In pseudo-relevance feedback, the two key factors affecting the retrieval performance most are the source from which expansion terms are generated and the method of ranking those expansion terms. In this paper, we present a novel unsupervised query expansion technique that utilizes keyphrases and POS phrase categorization. The keyphrases are extracted from the retrieved documents and weighted with an algorithm based on information gain and co-occurrence of phrases. The selected keyphrases are translated into Disjunctive Normal Form (DNF) based on the POS phrase categorization technique for better query refomulation. Furthermore, we study whether ontologies such as WordNet and MeSH improve the retrieval performance in conjunction with the keyphrases. We test our techniques on TREC 5, 6, and 7 as well as a MEDLINE collection. The experimental results show that the use of keyphrases with POS phrase categorization produces the best average precision.

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Available from: Il-Yeol Song
    • "Keyphrases are single words or phrases that provide a summary of a text (Tucker and Whittaker, 2009) and thus might improve searching (Song et al., 2006) in a large collection of texts. As manual extraction of keyphrases is a tedious task, a wide variety of keyphrase extraction approaches has been proposed. "

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    • "There is a vast amount of NLP literature on keyphrase extraction (Kim et al., 2010;). The semantic data provided by key-phrase extraction can be used as metadata for refining NLP applications, such as summarization (D'Avanzo and Magnini, 2005; Lawrie et al., 2001), text ranking (Mihalcea and Tarau, 2004), indexing (Medelyan and Witten, 2006), query expansion (Song et al., 2006), or document management and topic search (). The closest work to ours is (Turney, 1999 ) because they highlight key-phrases in the text to facilitate its skimming. "

    Full-text · Conference Paper · Jan 2014
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    • "Turney [19] extends the Kea algorithm by adding a coherence feature set that estimates the semantic relatedness of candidate keyphrases aiming to produce a more coherent set of keyphrases. Song et al. [15] use also a feature 'distance from first occurrence'. In addition, part of speech tags are used as features. "
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