NDLI: Discrimination of person names based on contexts co-occurrence

Content Provider	IEEE Xplore Digital Library
Author	Chen Chen Huilin Liu Liwei Zhang
Copyright Year	2011
Description	Author affiliation: College of Information Science and Engineering, Northeastern University, Shenyang, China (Chen Chen; Huilin Liu; Liwei Zhang)
Abstract	As the universal phenomenon that two people share a same name, picking out the relevant pages of a specific person from a mass of result documents which are related to multiple namesakes becomes a very troublesome and annoying thing. This paper proposes a contexts-co-occurrence-based method to deal with the name discrimination problem. Firstly, in order to consider both the global and local properties of a document, we extract important terms from the result collection through the method of combining the weight model and the windows model together. Secondly, base on those terms, the co-occurrence scores between any two terms are computed and then a contexts co-occurrence matrix is obtained. After that, according to the context matrix, we combine related context terms together to form several decision vectors. Finally, the similarities between any document vectors and decision vectors are computed through the VSM(vector space model) model. For a person name search task, the result documents can be divided into several groups automatically by the proposed method. The experiment result proves that our method can discriminate different persons who share the same name accurately and effectively.
Starting Page	2605
Ending Page	2609
File Size	280410
Page Count	5
File Format	PDF
ISBN	9781612841809
e-ISBN	9781612841816
DOI	10.1109/FSKD.2011.6020059
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Accuracy Computational modeling Social network services contexts co-occurrence matrix Feature extraction name discrimination Sparse matrices Context modeling decision vector
Content Type	Text
Resource Type	Article

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