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A natural language interface to a graph-based bibliographic information retrieval system.
- Source :
-
Data & Knowledge Engineering . Sep2017, Vol. 111, p73-89. 17p. - Publication Year :
- 2017
-
Abstract
- With the ever-increasing volume of scientific literature, there is a need for a natural language interface to bibliographic information retrieval systems to retrieve relevant information effectively. In this paper, we propose one such interface, NLI-GIBIR, which allows users to search for a variety of bibliographic data through natural language. NLI-GIBIR makes use of a novel framework applicable to graph-based bibliographic information retrieval systems in general. This framework incorporates algorithms/heuristics for interpreting and analyzing natural language bibliographic queries via a series of text- and linguistic-based techniques, including tokenization, named entity recognition, and syntactic analysis. We find that our framework, as implemented in NLI-GIBIR, can effectively represent and address complex bibliographic information needs. Thus, the contributions of this paper are as follows: First, to our knowledge, it is the first attempt to propose a natural language interface for graph-based bibliographic information retrieval. Second, we propose a novel customized natural language processing framework that integrates a few original algorithms/heuristics for interpreting and analyzing bibliographic queries. Third, we show that the proposed framework and natural language interface provide a practical solution for building real-world bibliographic information retrieval systems. Our experimental results show that the presented system can correctly answer 39 out of 40 example natural language queries with varying lengths and complexities. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0169023X
- Volume :
- 111
- Database :
- Academic Search Index
- Journal :
- Data & Knowledge Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 125232913
- Full Text :
- https://doi.org/10.1016/j.datak.2017.06.006