Text Segmentation Using Reiteration and Collocation

Improving Text Segmentation with Non-systematic Semantic Relation

Text segmentation is a fundamental problem in natural language processing, which has application in information retrieval, question answering, and text summarization. Almost previous works on unsupervised text segmentation are based on the assumption of lexical cohesion, which is indicated by relations between words in the two units of text. However, they only take into account the reiteration,...

Khmer Word Segmentation and Out - of - Vocabulary Words Detection Using Collocation Measurement of Repeated Characters Subsequences

Cohesion and Collocation: Using Context Vectors in Text Segmentation

Collocational word similarity is considered a source of text cohesion that is hard to measure and quantify. The work presented here explores the use of information from a training corpus in measuring word similarity and evaluates the method in the text segmentation task. An implementation, the VecTile system, produces similarity curves over texts using pre-compiled vector representations of the...

Using Collocations for Topic Segmentation and Link Detection

We present in this paper a method for achieving in an integrated way two tasks of topic analysis: segmentation and link detection. This method combines word repetition and the lexical cohesion stated by a collocation network to compensate for the respective weaknesses of the two approaches. We report an evaluation of our method for segmentation on two corpora, one in French and one in English, ...

Finding document topics for improving topic segmentation

Topic segmentation and identification are often tackled as separate problems whereas they are both part of topic analysis. In this article, we study how topic identification can help to improve a topic segmenter based on word reiteration. We first present an unsupervised method for discovering the topics of a text. Then, we detail how these topics are used by segmentation for finding topical si...