Conference Paper

Incorporating Document Keyphrases in Search Results.

Conference: 10th Americas Conference on Information Systems, AMCIS 2004, New York, NY, USA, August 6-8, 2004
Source: DBLP

ABSTRACT Effectiveness and efficiency of searching and returned results presentation is the key to a search engine. Before downloading and examining the document text, users usually first judge the relevance of a return hit to the query by looking at document metadata presented in the return result. However, the metadata coming with the return hit is usually not rich enough for users to predict the content of the document. Keyphrases provide a concise summary of a document's content, offering subject metadata characterizing and summarizing document. In this paper, we propose a mechanism of enriching the metadata of the return results by incorporating automatically extracted document keyphrases in each return hit. By looking at the keyphrases in each return hit, the user can predict the content of the document more easily, quickly, and accurately. The experimental results show that our solution may save users time up to 32% and users would like to use our proposed search interface with document keyphrases as part of the metadata of a return hit.

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    ABSTRACT: Keyphrases enable the reader to quickly determine whether the given article is suitable for the reader's digest. Keyphrases are also important for medical document retrieval and text mining research. Sometimes, the author-assigned Keyphrases or keywords available with the articles are too limited to represent the topical content of the articles. Many medical documents also do not come with author assigned Keyphrases. So, an automatic Keyphrase extraction process is highly desirable. Domain specific features can play an important role in domain specific Keyphrase extraction. This paper presents an automatic Keyphrase extraction method that exploits a number of domain-specific features to improve the Keyphrase extraction performance in medical domain.
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    ABSTRACT: Document keyphrases provide semantic metadata characterizing documents and producing an overview of the content of a document. They can be used in many text-mining and knowledge management related applications. This paper describes a Keyphrase Identification Program (KIP), which extracts document keyphrases by using prior positive samples of human identified domain keyphrases to assign weights to the candidate keyphrases. The logic of our algorithm is: the more keywords a candidate keyphrase contains and the more significant these keywords are, the more likely this candidate phrase is a keyphrase. To obtain prior positive inputs, KIP first populates its glossary database using manually identified keyphrases and keywords. It then checks the composition of all noun phrases of a document, looks up the database and calculates scores for all these noun phrases. The ones having higher scores will be extracted as keyphrases.
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    ABSTRACT: Keyphrases are the phrases, consisting of one or more words, representing the important concepts in the articles. Keyphrases are useful for a variety of tasks such as text summarization, automatic indexing, clustering/classification, text mining etc. This paper presents a hybrid approach to keyphrase extraction from medical documents. The keyphrase extraction approach presented in this paper is an amalgamation of two methods: the first one assigns weights to candidate keyphrases based on an effective combination of features such as position, term frequency, inverse document frequency and the second one assign weights to candidate keyphrases using some knowledge about their similarities to the structure and characteristics of keyphrases available in the memory (stored list of keyphrases). An efficient candidate keyphrase identification method as the first component of the proposed keyphrase extraction system has also been introduced in this paper. The experimental results show that the proposed hybrid approach performs better than some state-of-the art keyphrase extraction approaches.
    International Journal of Computer Applications 03/2013; 63(18). · 0.82 Impact Factor

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