KP-Miner: Participation in SemEval-2

Proceedings of the 5th International Workshop on Semantic Evaluation, ACL 2010, pages 190–193, Uppsala, Sweden, 15-16 July 2010. c⃝2010 Association for Computational Linguistics KP-Miner: Participation in SemEval-2 Samhaa R. El-Beltagy Cairo University Giza, Egypt. samhaa@computer.org Ahmed Rafea The American University in Cairo New Cairo, Egypt. rafea@aucegypt.edu Abstract This paper briefly describes the KP-Miner sys- tem which is a system developed for the ex- traction of keyphrases from English and Arab- ic documents, irrespective of their nature. The paper also outlines the performance of the sys- tem in the “Automatic Keyphrase Extraction from Scientific Articles” task which is part of SemEval-2. 1 Introduction KP-Miner (El-Beltagy, 2006) (El-Beltagy, 2009) is a system for the extraction of keyphrases from English and Arabic documents. When develop- ing the system, the goal was to build a general purpose keyphrase extraction system that can be easily configured by users based on their under- standing of the documents from which keyphras- es are to be extracted and without the need for any training documents or the use of any sophis- ticated natural language processing or linguistic tools. As such, the keyphrase extraction process in KP-Miner is an un-supervised one. When building a general purpose keyphrase extraction system, this was an important objective, as train- ing data is not always readily available for any type of data. The goal of entering the KP-Miner system into the SemEval-2 competition, was to see how well it will perform on a specific task, without making any changes in its default para- meters. 2 System Overview Keyphrase extraction in the KP-Miner system is a three step process: candidate keyphrase selec- tion, candidate keyphrase weight calculation and finally keyphrase refinement. Each of these steps, is explained in the following sub-sections. More details about the employed algorithm, and justification for using certain values for selected parameters, can be found in (El-Beltagy, 2009). 2.1 Candidate keyphrase selection In KP-Miner, a set of rules is employed in order to elicit candidate keyphrases. As a phrase will never be separated by punctuation marks within some given text and will rarely have stop words within it, the first condition a sequence of words has to display in order to be considered a candi- date keyphrase, is that it is not be separated by punctuation marks or stop words. A total of 187 common stopwords (the, then, in, above, etc) are used in the candidate keyphrase extraction step. After applying this first condition on any given document, too many candidates will be generated; some of which will make no sense to a human reader. To filter these out, two further conditions are applied. The first condition states that a phrase has to have appeared at least n times in the document from which keyphrases are to be extracted, in order to be considered a candidate keyphrase. This is called the least al- lowable seen frequency(lasf) factor and in the English version of the system, this is set to 3. However, if a document is short, n is decre- mented …

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