Phase-based information retrieval

Arampatzis, Avi; Tsoris, T.; Koster, C. H. A.; Weide, Th.P. van der

doi:10.1016/s0306-4573(98)00030-2

Cited by 46 publications

(21 citation statements)

References 7 publications

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“…They condense documents, offering a brief and precise description of their content. They have many further applications, including the classification or clustering of documents [12,28], search and browsing interfaces [10,11,13], retrieval engines [3,6,14] and thesaurus construction [15,18].…”

Section: Introductionmentioning

confidence: 99%

Human evaluation of Kea, an automatic keyphrasing system

Jones

Paynter

2001

Proceedings of the 1st ACM/IEEE-CS Joint Conference on Digital Libraries

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This paper describes an evaluation of the Kea automatic keyphrase extraction algorithm. Tools that automatically identify keyphrases are desirable because document keyphrases have numerous applications in digital library systems, but are costly and time consuming to manually assign. Keyphrase extraction algorithms are usually evaluated by comparison to author-specified keywords, but this methodology has several well-known shortcomings. The results presented in this paper are based on subjective evaluations of the quality and appropriateness of keyphrases by human assessors, and make a number of contributions. First, they validate previous evaluations of Kea that rely on author keywords. Second, they show Kea's performance is comparable to that of similar systems that have been evaluated by human assessors. Finally, they justify the use of author keyphrases as a performance metric by showing that authors generally choose good keywords.

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Section: Introductionmentioning

confidence: 99%

Human evaluation of Kea, an automatic keyphrasing system

Jones

Paynter

2001

Proceedings of the 1st ACM/IEEE-CS Joint Conference on Digital Libraries

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“…An IR system matches the bag of keywords in the user's query with the bag of keywords representing the documents to identify related documents and this approach suffers from a number of problems. All the above problems hurt the retrieval system in terms of precision and recall [8].…”

Section: Related Workmentioning

confidence: 99%

An improved Approach for Document Retrieval Using Suffix Trees

Sandhya¹,

Govardhan²,

Lalitha³

et al. 2011

IJACSA

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Abstract-Huge collection of documents is available at few mouse clicks. The current World Wide Web is a web of pages. Users have to guess possible keywords that might lead through search engines to the pages that contain information of interest and browse hundreds or even thousands of the returned pages in order to obtain what they want. In our work we build a generalized suffix tree for our documents and propose a search technique for retrieving documents based on a sort of phrase called word sequences. Our proposed method efficiently searches for a given phrase (with missing or additional words in between) with better performance.

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“…This parser can break a sentence into smaller syntactic phrases with respect to a given formal grammar, such as Noun Phrase, Verb Phrase, etc. Noun Phrase is the syntactic phrase that is most used for phrase indexing in IR experiments [5]- [7].…”

Section: B Natural Language Processing In Information Retrievalmentioning

confidence: 99%

Syntactic Information Retrieval

Park

Shim

2007

2007 IEEE International Conference on Granular Computing (GRC 2007)

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Natural language processing (NLP) techniques are believed to have the potential to aid information retrieval (IR) in terms of retrieval accuracy. In this paper we report a proof of concept study on a new approach to NLP-based IR that we propose. Documents and queries are represented as syntactic parse trees, which are generated by a natural language parser. Based on this tree structured representation of documents and queries, the matching between a document and a query is executed on their tree representations, with tree comparison as the key operation. An IR experiment is designed to test if this approach is feasible. Experimental results show that this approach is promising and has the potential to outperform the standard bag of words approach to information retrieval, especially in response to long queries.

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Phase-based information retrieval

Cited by 46 publications

References 7 publications

Human evaluation of Kea, an automatic keyphrasing system

Human evaluation of Kea, an automatic keyphrasing system

An improved Approach for Document Retrieval Using Suffix Trees

Syntactic Information Retrieval

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