Fidelia Ibekwe-Sanjuan

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A multiple-perspective cocitation analysis method is introduced for characterizing and interpreting the structure and dynamics of cocitation clusters. The method facilitates analytic and sense making tasks by integrating network visualization, spectral clustering, automatic cluster labeling, and text summarization. Cocitation networks are decomposed into(More)
Figure 1: Conflicting reviews of The Da Vinci Code: 1,738 positive (green) and 918 negative (red). ABSTRACT Understanding the nature and dynamics of conflicting opinions is a profound and challenging issue. In this paper we address several aspects of the issue through a study of more than 3,000 Amazon customer reviews of the controversial bestseller The Da(More)
This paper presents a three-level structuring of multiword terms (MWTs) basing on lexical inclusion, WordNet similarity and a clustering approach. Term clustering by automatic data analysis methods offers an interesting way of organizing a domain's knowledge structures, useful for several information-oriented tasks like science and technology watch,(More)
We present a methodology combining surface NLP and Machine Learning techniques for ranking asbtracts and generating summaries based on annotated corpora. The corpora were annotated with meta-semantic tags indicating the category of information a sentence is bearing (objective, findings, newthing, hypothesis, conclusion, future work, related work). The(More)
After extracting terms from a corpus of titles and abstracts in English, syntactic variation relations are identified amongst them in order to detect research topics. Three types of syntactic variations were studied : permutation, expansion and substitution. These syntactic variations yield other relations of formal and conceptual nature. Basing on a(More)
We present a system for mapping the structure of research topics in a corpus. TermWatch portrays the "aboutness" of a corpus of scientific and technical publications by bridging the gap between pure statistical approaches and symbolic techniques. In the present paper, an experiment on unsupervised textmining is performed on a corpus of scientific titles and(More)
We present a novel method for mapping thematic trends called "Classification by Preferential Clustered Link" (CPCL). This method clusters relevant textual units (terms) from a corpus of texts, based on meaningful linguistic relations (syntactic variations) identified amongst the units. Terms related through syntactic variations are represented in the form(More)
We address here the need to assist users in rapidly accessing the most important or strategic information in the text corpus by identifying sentences carrying specific information. More precisely, we want to identify contribution of authors of scientific papers through a categorization of sentences using rhetorical and lexical cues. We built local grammars(More)