A Comparative Survey on NLP/U Methodologies for Processing Multi-Documents

Mills, M.T.; Bourbakis, Nikolaos G.

doi:10.1142/s0218213012500170

Cited by 4 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The algorithm terminates when the AMI remains invariant in consecutive steps. In the literature, different notions of co-occurrence have been proposed (Church and Hanks 1990;Lin 1998;Manning et al 2008;Mills and Bourbakis 2012;Momtazi et al 2010;Zha 2002). They are, basically, co-occurrences in a sentence, window of text, syntactic relationship, and document.…”

Section: Co-occurrence Retrievalmentioning

confidence: 99%

Automatic Detection of Words Associations in Texts Based on Joint Distribution of Words Occurrences

Santoni

Pourabbas

2015

Computational Intelligence

View full text Add to dashboard Cite

In this article, we propose a novel approach for measuring word association based on the joint occurrences distribution in a text. Our approach relies on computing a sum of distances between neighboring occurrences of a given word pair and comparing it with a vector of randomly generated occurrences. The idea behind this assumption is that if the distribution of co-occurrences is close to random or if they tend to appear together less frequently than by chance, such words are not semantically related. We devise a distance function S that evaluates the words association rate. Using S, we build a concept tree, which provides a visual and comprehensive representation of keywords association in a text. In order to illustrate the effectiveness of our algorithm, we apply it to three different texts, showing the consistency and significance of the obtained results with respect to the semantics of documents. Finally, we compare the results obtained by applying our proposed algorithm with the ones achieved by both human experts and the co-occurrence correlation method. We show that our method is consistent with the experts' evaluation and outperforms with respect to the co-occurrence correlation method.

show abstract