Maximizing Semantic Relatedness to Perform Word Sense Disambiguation

Semantic Relatedness for Biomedical Word Sense Disambiguation

This paper presents a graph-based method for all-word word sense disambiguation of biomedical texts using semantic relatedness as edge weight. Semantic relatedness is derived from a term-topic co-occurrence matrix. The sense inventory is generated by the MetaMap program. Word sense disambiguation is performed on a disambiguation graph via a vertex centrality measure. The proposed method achieve...

SenseRelate: : TargetWord-A Generalized Framework for Word Sense Disambiguation

We have previously introduced a method of word sense disambiguation that computes the intended sense of a target word, using WordNet-based measures of semantic relatedness (Patwardhan et al., 2003). SenseRelate::TargetWord is a Perl package that implements this algorithm. The disambiguation process is carried out by selecting that sense of the target word which is most related to the context wo...

Path-Based Semantic Relatedness on Linked Data and Its Use to Word and Entity Disambiguation

Semantic relatedness and disambiguation are fundamental problems for linking text documents to the Web of Data. There are many approaches dealing with both problems but most of them rely on word or concept distribution over Wikipedia. They are therefore not applicable to concepts that do not have a rich textual description. In this paper, we show that semantic relatedness can also be accurately...

Using Measures of Semantic Relatedness for Word Sense Disambiguation

Word Sense Disambiguation with Semantic Networks

Word sense disambiguation (WSD) methods evolve towards exploring all of the available semantic information that word thesauri provide. In this scope, the use of semantic graphs and new measures of semantic relatedness may offer better WSD solutions. In this paper we propose a new measure of semantic relatedness between any pair of terms for the English language, using WordNet as our knowledge b...