In this paper lexical cohesion modeling is considered. We argue that to model lexical cohesion in connected texts it is not enough to find related words in a text. It is important to take into account relations between entities participating in the described situations. Consideration of this factor gives the possibility to develop more flexible lexical chaining algorithms and to construct lexic...

An efficient use of lexical cohesion is described for ranking text units according to their contribution in defining the meaning of a text (textual saliency), their ability to form a cohesive sub-text (textual connectivity) and the extent and effectiveness to which they address the different topics which characterize the subject matter of the text (topic aptness). A specific application is also...

W e use program slices to model module cohesion. For our purposes, a slice is a projection of program text that includes only the data tokens relevant t o one output. W e define six cohesion metrics in terms of these slices, and evaluate the effects of classes of module changes on these metrics. W e find that the effects on cohesion metrics are notably more predictable when the changes result f...

A quantitative representation of discourse structure can be computed by measuring lexical cohesion relations among adjacent text elements. These representations have previously been proposed to deal with sub-topic text segmentation. In a parallel corpus, similar representations can be derived for versions of a text in various languages. These can be used for parallel segmentation and as an alte...

We explored the possibility of predicting learners’ affective states (boredom, flow/engagement, confusion, and frustration) by monitoring variations in the cohesiveness of tutorial dialogues during interactions with AutoTutor, an intelligent tutoring system with conversational dialogues. Multiple measures of cohesion (e.g., pronouns, connectives, semantic overlap, causal cohesion, coreference) ...

Preliminary results from an experimental study of readers’ perceptions of lexical cohesion and lexical semantic relations in text are presented. Readers agree on a common “core” of groups of related words and exhibit individual differences. The majority of relations reported are “non-classical” (not hyponymy, meronymy, synonymy, or antonymy). A group of commonly used relations is presented. The...

This paper presents a method for the automatic detection and correction of malapropism errors found in documents using the WordNet lexical database, a search engine (Google) and a paronyms dictionary. The malapropisms detection is based on the evaluation of the cohesion of the local context using the search engine, while the correction is done using the whole text cohesion evaluated in terms of...