Learning Taxonomy for Text Segmentation by Formal Concept Analysis

In this paper the problems of deriving a taxonomy from a text and concept-oriented text segmentation are approached. Formal Concept Analysis (FCA) method is applied to solve both of these linguistic problems. The proposed segmentation method offers a conceptual view for text segmentation, using a context-driven clustering of sentences. The Concept-oriented Clustering Segmentation algorithm (COCS) is based on k-means linear clustering of the sentences. Experimental results obtained using COCS algorithm are presented.

A chain dictionary method for Word Sense Disambiguation and applications

A large class of unsupervised algorithms for Word Sense Disambiguation (WSD) is that of dictionary-based methods. Various algorithms have as the root Lesk's algorithm, which exploits the sense definitions in the dictionary directly. Our approach uses the lexical base WordNet for a new algorithm originated in Lesk's, namely "chain algorithm for disambiguation of all words", CHAD. We show how translation from a language into another one and also text entailment verification could be accomplished by this disambiguation.

Textual Entailment Recognizing by Theorem Proving Approach

In this paper we present two original methods for recognizing textual inference. First one is a modified resolution method such that some linguistic considerations are introduced in the unification of two atoms. The approach is possible due to the recent methods of transforming texts in logic formulas. Second one is based on semantic relations in text, as presented in WordNet. Some similarities between these two methods are remarked.