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General Information
    • ISSN: 2010-3700 (Online)
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.

IJMLC 2013 Vol.3(2): 195-200 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.301

A recursive TF-ISF Based Sentence Retrieval Method with Local Context

Alen Doko, Maja Štula, and Darko Stipaničev
Abstract—Sentence retrieval consists of retrieving relevant sentences from a document base in response to a query. Question answering, novelty detection, summarization, opinion mining and information provenance make use of sentence retrieval. Most of the sentence retrieval methods are trivial adaptations of document retrieval methods. However some newer sentence retrieval methods based on the language modeling framework successfully use some kind of context of sentences. Unlike that there is no successful improvement of the TF-ISF method that takes into account the context of sentences. In this paper we propose a recursive TF-ISF based method that takes into account the local context of a sentence. The context is considered the previous and next sentence of current sentence. We compared the new method to the TF-ISF baseline and to an earlier unsuccessful method that also incorporates a similar context into TF-ISF. We got statistically significant improvements of the results in comparison to both of the methods. Additional benefit of our method is the clear explicit model of the context that will allow us to automatically generate a document representation with context suitable for sentence retrieval which is important for our future work.

Index Terms—Context, document representation, TF-ISF, sentence retrieval, recursion.

A. Doko is with the JP Croatian Telecommunications d.o.o. Mostar, Mostar, Bosnia and Herzegovina (e-mail: alen.doko@hteronet.ba).
M. Štula and D. Stipaničev are with the Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Split, Croatia (e-mail: maja.stula@fesb.hr; darko.stipanicev@fesb.hr).


Cite:Alen Doko, Maja Štula, and Darko Stipaničev, "A recursive TF-ISF Based Sentence Retrieval Method with Local Context," International Journal of Machine Learning and Computing vol. 3, no. 2, pp. 195-200, 2013.

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