Exploiting clustering and phrases for context-based information retrieval

Anick, Peter; Vaithyanathan, Shivakumar

doi:10.1145/258525.258601

Cited by 68 publications

(26 citation statements)

References 30 publications

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“…The most profound extension of the present study from former visualization studies is therefore the novel step of natural language parsing of noun phrases from the citation contexts. The rationale for the use of noun phrases as key conceptual phrases is that they represent more meaningful concepts than individual words (e.g., ANICK & VAITHYANATHAN, 1997). Noun phrases are believed to be content bearing units and thus good indicators of the content of a text.…”

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confidence: 99%

Concept symbols revisited: Naming clusters by parsing and filtering of noun phrases from citation contexts of concept symbols

Schneider

2006

Scientometrics

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The present study presents a semi-automatic method for parsing and filtering of noun phrases from citation contexts of concept symbols. The purpose of the method is to extract contextual, agreed upon, and pertinent noun phrases, to be used in visualization studies for naming clusters (concept groups) or concept symbols. The method is applied in a case study, which forms part of a larger dissertation work concerning the applicability of bibliometric methods for thesaurus construction. The case study is carried out within periodontology, a specialty area of dentistry. The result of the case study indicates that the method is able to identify highly important noun phrases, and that these phrases accurately describe their parent clusters. Hence, the method is able to reduce the labour intensive work of manual citation context analysis, though further refinements are still needed.

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confidence: 99%

Concept symbols revisited: Naming clusters by parsing and filtering of noun phrases from citation contexts of concept symbols

Schneider

2006

Scientometrics

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“…Phrase indexing has been studied by many previous studies (Gutwin et al 2003;Fagan 1989;Anick and Vaithyanathan 1997;Arampatzis et al 1998). These studies have shown that phrase indexing can improve retrieval effectiveness.…”

Section: Discussionmentioning

confidence: 99%

“…Because document keyphrases as described in our study are a subset of a document's noun phrases, it is very possible that keyphrases can also be used in the same applications of noun phrases and better results may be acquired. Many studies utilizing noun phrases focus on the applications of retrieval systems and browsing interfaces (Liddy and Myaeng 1993;Anick and Tiperneni 1999;Inf Retrieval (2008) 11:229-249 231 Wacholder et al 2001Arampatzis et al 1998); some others explore their applicatuions on document classification and clustering (Anick and Vaithyanathan 1997;Zamir and Etzioni 1999). Anick and Vaithyanathan (1997) describe a model of context-based information retrieval.…”

Section: Applications Of Keyphrasesmentioning

confidence: 99%

“…Many studies utilizing noun phrases focus on the applications of retrieval systems and browsing interfaces (Liddy and Myaeng 1993;Anick and Tiperneni 1999;Inf Retrieval (2008) 11:229-249 231 Wacholder et al 2001Arampatzis et al 1998); some others explore their applicatuions on document classification and clustering (Anick and Vaithyanathan 1997;Zamir and Etzioni 1999). Anick and Vaithyanathan (1997) describe a model of context-based information retrieval. In their model, clustering and phrasal information are used together within the context of a retrieval interface.…”

Section: Applications Of Keyphrasesmentioning

confidence: 99%

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Document keyphrases as subject metadata: incorporating document key concepts in search results

2008

Inf Retrieval

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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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“…Organizing text collections for enabling the retrieval by content [2,14,15,16] and searching [4] can be accomplished by using text collection clustering. Cutting et al used a clustering technique that supports an iterative searching interface by dynamically scattering a document collection into smaller semantic clusters.…”

Section: Text Clustering For Ir By Contentmentioning

confidence: 99%

Prototype-matching system for allocating conference papers

Antonina

Barbro

Hannu

et al. 2003

36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of The

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Conferences on applied research require more complicated taxonomy than traditional organization of conferences by tracks. A topic of a paper, submitted to a conference on the applied research and the keywords, outlined by authors can be discussed in more than one proposed conference track. Sorting out the papers submitted to a scientific conference in the proposed categories and tracks is becoming a nontrivial task. Conference organizing committees try to schedule submitted papers very carefully to increase the success rate of the conference. For example, the organizers of The Hawaii International Conference on System Science 2001(HICSS-34) allocated the theme similarities in papers that were submitted into different tracks and identified 6 cross-track themes to schedule them appropriately.In this paper, we offer a prototype matching system for text retrieval by content and try it out on the HICSS 34 conference proceeding. On the one hand, the system assists the conference organizers to automatically establish semantic similarities among papers and allocate them into common themes. On the other hand, the system assists the attendees to retrieve the papers from the conference proceedings based on their content similarities. A user can take an abstract or a paragraph from an interesting paper, and use it as a prototype query. The information system is based on document preprocessing, "smart" document encoding and prototype-matching clustering of a text collection.

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Exploiting clustering and phrases for context-based information retrieval

Cited by 68 publications

References 30 publications

Concept symbols revisited: Naming clusters by parsing and filtering of noun phrases from citation contexts of concept symbols

Concept symbols revisited: Naming clusters by parsing and filtering of noun phrases from citation contexts of concept symbols

Document keyphrases as subject metadata: incorporating document key concepts in search results

Prototype-matching system for allocating conference papers

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