BUAP: An Unsupervised Approach to Automatic Keyphrase Extraction from Scientific Articles

Proceedings of the 5th International Workshop on Semantic Evaluation, ACL 2010, pages 174–177, Uppsala, Sweden, 15-16 July 2010. c⃝2010 Association for Computational Linguistics BUAP: An Unsupervised Approach to Automatic Keyphrase Extraction from Scientific Articles Roberto Ortiz, David Pinto, Mireya Tovar Faculty of Computer Science, BUAP Puebla, Mexico korn resorte2003@hotmail.com, {dpinto, mtovar}@cs.buap.mx H´ector Jim´enez-Salazar Information Technologies Dept., UAM DF, Mexico hgimenezs@gmail.com Abstract In this paper, it is presented an unsuper- vised approach to automatically discover the latent keyphrases contained in scien- tific articles. The proposed technique is constructed on the basis of the combi- nation of two techniques: maximal fre- quent sequences and pageranking. We evaluated the obtained results by using micro-averaged precision, recall and F- scores with respect to two different gold standards: 1) reader’s keyphrases, and 2) a combined set of author’s and reader’s keyphrases. The obtained results were also compared against three different base- lines: one unsupervised (TF-IDF based) and two supervised (Na¨ıve Bayes and Maximum Entropy). 1 Introduction The task of automatic keyphrase extraction has been studied for several years. Firstly, as semantic metadata useful for tasks such as summarization (Barzilay and Elhadad, 1997; Lawrie et al., 2001; DAvanzo and Magnini, 2005), but later rec- ognizing the impact that good keyphrases would have on the quality of various Nat- ural Language Processing (NLP) applica- tions (Frank et al., 1999; Witten et al., 1999; Turney, 1999; Barker and Corrnacchia, 2000; Medelyan and Witten, 2008). Thus, the selection of important, topical phrases from within the body of a document may be used in order to improve the performance of systems dealing with different NLP problems such as, clustering, question-answering, named entity recognition, information retrieval, etc. In general, a keyphrase may be considered as a sequence of one or more words that capture the main topic of the document, as that keyphrase is expected to represent one of the key ideas ex- pressed by the document author. Following the previously mentioned hypothesis, we may take ad- vantage of two different techniques of text analy- sis: maximal frequent sequences to extract a se- quence of one or more words from a given text, and pageranking, expecting to extract those word sequences that represent the key ideas of the au- thor. The interest on extracting high quality keyphrases from raw text has motivated forums, such as SemEval, where different systems may evaluate their performances. The purpose of SemEval is to evaluate semantic analysis systems. In particular, in this paper we are reporting the results obtained in Task #5 of SemEval-2 2010, which has been named: “Automatic Keyphrase Extraction from Scientific Articles”. We focused this paper on the description of our approach and, therefore, we do not describe into detail the task nor the dataset used. For more information about this information read the “Task #5 Description paper”, also published in this proceedings volume (Nam Kim et al., 2010). The rest of this paper is structured as follows. Section 2 describes into detail the components of the proposed approach. In Section 3 it …

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