A new algorithm for construction specific field terms using co-occurrence words information

Readers can know the subject of many document fields by reading only some specific words called field association (FA) terms. It is very important to construct these FA terms to decide correctly the document fields from few words information in part of file. The field can be decided efficiency if th...

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Main Authors: Atlam, E.-S., Ghada, E., Fuketa, M., Aoe, J.
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
Costs
Data compression
Data mining
Frequency
Information science
Intelligent systems
Natural language processing
Partitioning algorithms
Thesauri
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creator Atlam, E.-S.
Ghada, E.
Fuketa, M.
Aoe, J.
description Readers can know the subject of many document fields by reading only some specific words called field association (FA) terms. It is very important to construct these FA terms to decide correctly the document fields from few words information in part of file. The field can be decided efficiency if the number of these FA terms is many and the frequency rate is high. If the number of level I (words that direct connect to terminal fields) FA word is limited, old methods can not determine the documents tiled easily and fast, special when there is a small number of corpus documents. This paper proposes a new method for deciding FA terms using the weight of co-occurrence words and declinable words which related to a narrow association category with eliminating FA terms ambiguity. Moreover, efficient FA terms are difficult to be extracted only by the information of the frequency of them. This paper proposed a new efficient method using new cooccurrence words weight which makes precision and recall are higher than the case of degree of frequency.
doi_str_mv 10.1109/MWSCAS.2003.1562453
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subjects Clustering algorithms
Costs
Data compression
Data mining
Frequency
Information science
Intelligent systems
Natural language processing
Partitioning algorithms
Thesauri
title A new algorithm for construction specific field terms using co-occurrence words information
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