Document keyphrases as subject metadata: incorporating document key concepts in search results
Most search engines display some document metadata, such as title, snippet and URL, in conjunction with the returned hits to aid users in determining documents. However, metadata is usually fragmented pieces of information that, even when combined, does not provide an overview of a returned document...
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| Published in | Information retrieval (Boston) Vol. 11; no. 3; pp. 229 - 249 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.06.2008
Springer Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1386-4564 1573-7659 |
| DOI | 10.1007/s10791-008-9044-1 |
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| Abstract | Most search engines display some document metadata, such as title, snippet and URL, in conjunction with the returned hits to aid users in determining documents. However, metadata is usually fragmented pieces of information that, even when combined, does not provide an overview of a returned document. In this paper, we propose a mechanism of enriching metadata of the returned results by incorporating automatically extracted document keyphrases with each returned hit. We hypothesize that keyphrases of a document can better represent the major theme in that document. Therefore, by examining the keyphrases in each returned hit, users can better predict the content of documents and the time spent on downloading and examining the irrelevant documents will be reduced substantially. |
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| AbstractList | Most search engines display some document metadata, such as title, snippet and URL, in conjunction with the returned hits to aid users in determining documents. However, metadata is usually fragmented pieces of information that, even when combined, does not provide an overview of a returned document. In this paper, we propose a mechanism of enriching metadata of the returned results by incorporating automatically extracted document keyphrases with each returned hit. We hypothesize that keyphrases of a document can better represent the major theme in that document. Therefore, by examining the keyphrases in each returned hit, users can better predict the content of documents and the time spent on downloading and examining the irrelevant documents will be reduced substantially. [PUBLICATION ABSTRACT] Most search engines display some document metadata, such as title, snippet and URL, in conjunction with the returned hits to aid users in determining documents. However, metadata is usually fragmented pieces of information that, even when combined, does not provide an overview of a returned document. In this paper, we propose a mechanism of enriching metadata of the returned results by incorporating automatically extracted document keyphrases with each returned hit. We hypothesize that keyphrases of a document can better represent the major theme in that document. Therefore, by examining the keyphrases in each returned hit, users can better predict the content of documents and the time spent on downloading and examining the irrelevant documents will be reduced substantially. |
| Author | Wu, Yi-fang Brook Li, Quanzhi |
| Author_xml | – sequence: 1 givenname: Yi-fang Brook surname: Wu fullname: Wu, Yi-fang Brook email: wu@njit.edu organization: Information Systems Department, New Jersey Institute of Technology – sequence: 2 givenname: Quanzhi surname: Li fullname: Li, Quanzhi organization: Avaya Labs Research, Avaya, Inc |
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| CitedBy_id | crossref_primary_10_1007_s10506_021_09296_2 crossref_primary_10_3745_JIPS_2012_8_4_693 crossref_primary_10_3745_JIPS_2013_9_4_602 crossref_primary_10_1002_int_20448 crossref_primary_10_1080_19386389_2011_629958 crossref_primary_10_1177_0165551512472138 crossref_primary_10_4018_ijtd_2014040103 |
| Cites_doi | 10.1145/258525.258601 10.1145/379437.379468 10.3115/1075671.1075795 10.1080/095281399146463 10.1145/122860.122864 10.1145/312624.312671 10.1145/1125857.1125861 10.1145/1240624.1240690 10.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B 10.1023/A:1009976227802 10.1002/asi.10068 10.1145/312624.312670 10.1016/S0306-4573(98)00030-2 10.1002/(SICI)1097-4571(2000)51:4<352::AID-ASI5>3.0.CO;2-8 10.1002/asi.20341 10.2307/1266673 |
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| Keywords | Document metadata Keyphrase extraction Document surrogate Search interface Document keyphrase Document surrogate .Search interface Query expansion Information retrieval Keyphrase extraction .Document metadata .Document keyphrase Knowledge extraction Search algorithm |
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| References | Witten, I. H. (1999). Browsing around a digital library. In Proceeding of Australasian Computer Science Conference (pp. 1–14). Auckland, New Zealand. BrillE.Transformation-based error-driven learning and natural language processing: A case study in part-of-speech taggingComputational Linguistics1995214543565 KosovacB.VanierD. J.FroeseT. M.Use of keyphrase extraction software for creation of an AEC/FM thesaurusJournal of Information Technology in Construction200052536 DavisG. B.Blackwell encyclopedic dictionary of management information system1997Malden, MABlackwell Publishing Wacholder, N., Evans, D. K., & Klavans, J. L. (2001). Automatic identification and organization of index terms for interactive browsin. In Proceedings of the First ACM/IEEE-CS Joint Conference on Digital Libraries (pp. 126–134). Roanoke, VA, USA. Jones, S., & Mahoui, M. (2000). Hierarchical document clustering using automatically extracted keyphrase. In Proceeding of the Third International Asian Conference on Digital Libraries (pp. 113–120). Seoul, Korea. Jones, S., & Staveley, M. (1999). Phrasier: A system for interactive document retrieval using keyphrases. In Proceedings of SIGIR’99: The 22nd International Conference on Research and Development in Information Retrieval (pp. 160–167). Berkeley, CA: ACM Press. GoodmanL. A.On simultaneous confidence intervals for multinomial proportionsTechnometrics196572472540131.1770110.2307/1266673 Muñoz, M., Punyakanok, V., Roth, D., & Zimak, D (1999). A learning approach to shallow parsing. Proceedings of EMNLP/WVLC-99, University of Maryland, MD. Croft, B., Turtle, H., & Lewis, D. (1991). The use of phrases and structured queries in information retrieval. In A. Bookstein, Y. Chiaramella, G. Salton, & V. V. Raghavan, (Eds.), Proceeding of SIGIR'91: The 14th Annual International Conference on Research and Development in Information Retrieval, Philadelphia (pp. 32–45). New York: ACM Press. Sang, E. F (2000). Noun phrase representation by system combination. Proceedings of ANLP-NAACL 2000, Seattle, WA. Anick, P., & Tiperneni, S. (1999). The paraphrase search assistant: Terminological feedback for iterative information seeking. In Proceedings of SIGIR’99: The 22nd Annual International Conference on Research and Development in Information Retrieval (pp. 153–159). Berkeley, CA, USA. Frank, E., Paynter, G., Witten, I., Gutwin, C., & Nevill-Manning, C. (1999). Domain-specific keyphrase extraction. In Proceeding of the Sixteenth International Joint Conference on Artificial Intelligence (pp. 668–673). San Mateo, CA. ZamirO.EtzioniO.Grouper: A dynamic clustering interface to Web search resultComputer Networks and ISDN Systems19993111–1613611374 Liddy, E. D., & Myaeng, S. H. (1993). DR-LINK’s linguistic-conceptual approach to document detection. In Proceedings of First Text Retrieval Conference (TREC-1)(pp. 113–130). Washington, D.C., USA. Anick, P., & Vaithyanathan, S. (1997). Exploiting clustering and phrases for context-based information retrieval. In Proceedings of SIGIR’97: The 20th Annual International Conference on Research and Development in Information Retrieval (pp. 314–322). Philadelphia, PA: ACM Press. JonesS.PaynterG.Automatic extraction of document keyphrases for use in digital libraries: evaluation and applicationsJournal of the American Society for Information Science and Technology200253865367710.1002/asi.10068 McDonaldD.ChenH.Summary in context: Searching versus browsingACM Transaction on Information Systems200624111114110.1145/1125857.1125861 Ramshaw L. A., & Marcus, M. P (1995). Text chunking using transformation-based learning. In Proceedings of the Third Workshop on Very Large Corpora. Cambridge, MA. WuY. B.LiQ.BotR.ChenX.Finding nuggets in documents: A machine learning approachJournal of the American society for information and technology (JASIST)200657674075210.1002/asi.20341 Cutrell, E., & Guan, Z. (2007). What are you looking for? An eye-tracking study of information usage in Web search. In Proceedings of CHI 2007, April 28–May 3, 2007, San Jose, CA. TolleK. M.ChenH.Comparing noun phrasing techniques for use with medical digital library toolsJournal of the American Society for Information Science200051435237010.1002/(SICI)1097-4571(2000)51:4<352::AID-ASI5>3.0.CO;2-8 TurneyP. D.Learning algorithm for keyphrase extractionInformation Retrieval20002430333610.1023/A:1009976227802 FaganJ. L.The effectiveness of a nonsyntactic approach to automatic phrase indexing for document retrievalJournal of the American Society for Information Science198940211513210.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B GutwinC.PaynterG.WittenI.Nevill-ManningC.FrankE.Improving browsing in digital libraries with keyphrase indexesJournal of. Decision Support Systems2003271–281104 Cardie, C., & Pierce, D. (1999). The role of lexicalization and pruning for base noun phrase grammars. In Proceedings of the Sixteenth National Conference on Artificial Intelligence (pp. 423–430). Orlando, Florida. ArgamonS.DaganI.KrymolowskiY.A memory-based approach to learning shallow natural language patternsJournal of Experimental and Theoretical Artificial Intelligence (JETAI)199911336939010.1080/095281399146463 ArampatzisA. T.TsorisT.KosterC. H.WeideT.Phrase-based information retrievalInformation Processing and Management199834669370710.1016/S0306-4573(98)00030-2 O. Zamir (9044_CR28) 1999; 31 Y. B. Wu (9044_CR27) 2006; 57 D. McDonald (9044_CR18) 2006; 24 B. Kosovac (9044_CR16) 2000; 5 9044_CR7 9044_CR25 S. Argamon (9044_CR4) 1999; 11 9044_CR24 C. Gutwin (9044_CR12) 2003; 27 9044_CR21 S. Jones (9044_CR14) 2002; 53 9044_CR20 A. T. Arampatzis (9044_CR3) 1998; 34 K. M. Tolle (9044_CR22) 2000; 51 9044_CR2 9044_CR1 L. A. Goodman (9044_CR11) 1965; 7 9044_CR6 9044_CR19 J. L. Fagan (9044_CR76) 1989; 40 9044_CR17 E. Brill (9044_CR5) 1995; 21 P. D. Turney (9044_CR23) 2000; 2 9044_CR15 9044_CR13 9044_CR10 9044_CR75 G. B. Davis (9044_CR8) 1997 |
| References_xml | – reference: KosovacB.VanierD. J.FroeseT. M.Use of keyphrase extraction software for creation of an AEC/FM thesaurusJournal of Information Technology in Construction200052536 – reference: Jones, S., & Staveley, M. (1999). Phrasier: A system for interactive document retrieval using keyphrases. In Proceedings of SIGIR’99: The 22nd International Conference on Research and Development in Information Retrieval (pp. 160–167). Berkeley, CA: ACM Press. – reference: Anick, P., & Vaithyanathan, S. (1997). Exploiting clustering and phrases for context-based information retrieval. In Proceedings of SIGIR’97: The 20th Annual International Conference on Research and Development in Information Retrieval (pp. 314–322). Philadelphia, PA: ACM Press. – reference: Cardie, C., & Pierce, D. (1999). The role of lexicalization and pruning for base noun phrase grammars. In Proceedings of the Sixteenth National Conference on Artificial Intelligence (pp. 423–430). Orlando, Florida. – reference: FaganJ. L.The effectiveness of a nonsyntactic approach to automatic phrase indexing for document retrievalJournal of the American Society for Information Science198940211513210.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B – reference: GoodmanL. A.On simultaneous confidence intervals for multinomial proportionsTechnometrics196572472540131.1770110.2307/1266673 – reference: DavisG. B.Blackwell encyclopedic dictionary of management information system1997Malden, MABlackwell Publishing – reference: Sang, E. F (2000). Noun phrase representation by system combination. Proceedings of ANLP-NAACL 2000, Seattle, WA. – reference: Muñoz, M., Punyakanok, V., Roth, D., & Zimak, D (1999). A learning approach to shallow parsing. Proceedings of EMNLP/WVLC-99, University of Maryland, MD. – reference: Wacholder, N., Evans, D. K., & Klavans, J. L. (2001). Automatic identification and organization of index terms for interactive browsin. In Proceedings of the First ACM/IEEE-CS Joint Conference on Digital Libraries (pp. 126–134). Roanoke, VA, USA. – reference: ArgamonS.DaganI.KrymolowskiY.A memory-based approach to learning shallow natural language patternsJournal of Experimental and Theoretical Artificial Intelligence (JETAI)199911336939010.1080/095281399146463 – reference: Jones, S., & Mahoui, M. (2000). Hierarchical document clustering using automatically extracted keyphrase. In Proceeding of the Third International Asian Conference on Digital Libraries (pp. 113–120). Seoul, Korea. – reference: Witten, I. H. (1999). Browsing around a digital library. In Proceeding of Australasian Computer Science Conference (pp. 1–14). Auckland, New Zealand. – reference: BrillE.Transformation-based error-driven learning and natural language processing: A case study in part-of-speech taggingComputational Linguistics1995214543565 – reference: TurneyP. D.Learning algorithm for keyphrase extractionInformation Retrieval20002430333610.1023/A:1009976227802 – reference: ZamirO.EtzioniO.Grouper: A dynamic clustering interface to Web search resultComputer Networks and ISDN Systems19993111–1613611374 – reference: Croft, B., Turtle, H., & Lewis, D. (1991). The use of phrases and structured queries in information retrieval. In A. Bookstein, Y. Chiaramella, G. Salton, & V. V. Raghavan, (Eds.), Proceeding of SIGIR'91: The 14th Annual International Conference on Research and Development in Information Retrieval, Philadelphia (pp. 32–45). New York: ACM Press. – reference: GutwinC.PaynterG.WittenI.Nevill-ManningC.FrankE.Improving browsing in digital libraries with keyphrase indexesJournal of. Decision Support Systems2003271–281104 – reference: McDonaldD.ChenH.Summary in context: Searching versus browsingACM Transaction on Information Systems200624111114110.1145/1125857.1125861 – reference: ArampatzisA. T.TsorisT.KosterC. H.WeideT.Phrase-based information retrievalInformation Processing and Management199834669370710.1016/S0306-4573(98)00030-2 – reference: WuY. B.LiQ.BotR.ChenX.Finding nuggets in documents: A machine learning approachJournal of the American society for information and technology (JASIST)200657674075210.1002/asi.20341 – reference: Ramshaw L. A., & Marcus, M. P (1995). Text chunking using transformation-based learning. In Proceedings of the Third Workshop on Very Large Corpora. Cambridge, MA. – reference: JonesS.PaynterG.Automatic extraction of document keyphrases for use in digital libraries: evaluation and applicationsJournal of the American Society for Information Science and Technology200253865367710.1002/asi.10068 – reference: Frank, E., Paynter, G., Witten, I., Gutwin, C., & Nevill-Manning, C. (1999). Domain-specific keyphrase extraction. In Proceeding of the Sixteenth International Joint Conference on Artificial Intelligence (pp. 668–673). San Mateo, CA. – reference: Liddy, E. D., & Myaeng, S. H. (1993). DR-LINK’s linguistic-conceptual approach to document detection. In Proceedings of First Text Retrieval Conference (TREC-1)(pp. 113–130). Washington, D.C., USA. – reference: Cutrell, E., & Guan, Z. (2007). What are you looking for? An eye-tracking study of information usage in Web search. In Proceedings of CHI 2007, April 28–May 3, 2007, San Jose, CA. – reference: TolleK. M.ChenH.Comparing noun phrasing techniques for use with medical digital library toolsJournal of the American Society for Information Science200051435237010.1002/(SICI)1097-4571(2000)51:4<352::AID-ASI5>3.0.CO;2-8 – reference: Anick, P., & Tiperneni, S. (1999). The paraphrase search assistant: Terminological feedback for iterative information seeking. In Proceedings of SIGIR’99: The 22nd Annual International Conference on Research and Development in Information Retrieval (pp. 153–159). 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| Title | Document keyphrases as subject metadata: incorporating document key concepts in search results |
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