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Research on Term Weighting Based on MapReduce

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Abstract

Term recognition is widely used in the ontology construction,dictionary construction and other fields.And term weighting is a key step in the term recognition.In this paper,several improvements have been made to TF-IDF algorithm,e.g.,the length of terms is considered in weighting,also with terms' correlations to documentation set.The candidate term weight is calculated in a distributed manner based on MapReduce on Hadoop.Experimental results show that the method proposed not only simplifies the steps of term weighting,but also improves the efficiency of the algorithm.

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What this paper is about

Term recognition is widely used in the ontology construction,dictionary construction and other fields.And term weighting is a key step in the term recognition.In this paper,several improvements have been made to TF-IDF algorithm,e.g.,the length of terms is considered in weighting,also with terms' correlations to documentation set.The candidate term weight is calculated in a distributed manner based on MapReduce on Hadoop.Experimental results show that the method proposed not only simplifies the steps of term weighting,but also improves the efficiency of the algorithm.

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Available abstract

Term recognition is widely used in the ontology construction,dictionary construction and other fields.And term weighting is a key step in the term recognition.In this paper,several improvements have been made to TF-IDF algorithm,e.g.,the length of terms is considered in weighting,also with terms' correlations to documentation set.The candidate term weight is calculated in a distributed manner based on MapReduce on Hadoop.Experimental results show that the method proposed not only simplifies the steps of term weighting,but also improves the efficiency of the algorithm.

Key concepts: Weighting, Term (time), Computer science, Key (lock), Set (abstract data type), Data mining, Documentation, Ontology

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