NDLI: Harnessing Semantics for Answer Sentence Retrieval

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Content Provider	ACM Digital Library
Author	Sanderson, Mark Spina, Damiano Scholer, Falk Chen, Ruey-Cheng Croft, W. Bruce
Abstract	Finding answer passages from the Web is a challenging task. One major difficulty is to retrieve sentences that may not have many terms in common with the question. In this paper, we experiment with two semantic approaches for finding non-factoid answers using a learning-to-rank retrieval setting. We show that using semantic representations learned from external resources such as Wikipedia or Google News may substantially improve the quality of top-ranked retrieved answers.
Starting Page	21
Ending Page	27
Page Count	7
File Format	PDF
ISBN	9781450337908
DOI	10.1145/2810133.2810136
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-10-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Answer finding Sentence retrieval Non-factoid question answering Semantic representations
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Harnessing Semantics for Answer Sentence Retrieval