NDLI: A New Approach for Better Document Retrieval and Classification Performance Using Supervised WSD and Concept Graph

Content Provider	IEEE Xplore Digital Library
Author	Soltanpoor, R. Mohsenzadeh, M. Mohaqeqi, M.
Copyright Year	2010
Abstract	Word Sense Disambiguation (WSD) is main task in the area of natural language processing (NLP). Supervised WSD methods are shown to be more effective than other WSD methods with the limitation of the size of manual annotated learning set. On the other hand, Concept graph is a weighted graph with each of its edges representing the relationships between concepts (relevancy of each pair of concepts). In this paper, we propose a method to improve the retrieval and classification performance of documents from different sources by means of concept graph. In our method, some features are initially selected from a training set by applying a well-known feature selection algorithm. Then, by injecting suggested relevant words for each class from the concept graph, a more enriched feature set is produced to apply to the test set. Our experimental results exhibit an improvement of 14.6% and 18.4% (few and more term injection evaluations, respectfully) in classification and also some improvements in retrieval performance
Starting Page	32
Ending Page	38
File Size	292939
Page Count	7
File Format	PDF
ISBN	9781424479634
DOI	10.1109/ICIIC.2010.63
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-05
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Context Feature Selection Naïve Supervised WSD Text categorization Support vector machine classification Feature extraction Concept Graph Classification algorithms Information Retrieval Indexing
Content Type	Text
Resource Type	Article

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