Background: Multiple Sclerosis (MS) syndrome is a type of Immune-Mediated disorder in the central nervous system (CNS) which destroys myelin sheaths, and results in plaque (lesion) formation in the brain. From the clinical point of view, investigating and monitoring information such as position, volume, number, and changes of these plaques are integral parts of the controlling process this dise...

Topic segmentation, which aims to fmd the boundaries between topic blocks in a text, is an important task for semantic analysis of texts. Although different solutions have been proposed for the task, many limitations and difficulties exist in the approaches. In particular most of the methods do not work well for such case as short texts, internet news and student's writings. In this paper, we f...

In a landmark study, Jusczyk & Aslin (1995) demonstrated that English-learning infants are able to segment words from continuous speech at 7.5 months of age. In the current study, we explored the possibility that infants segment words from the edges of utterances more readily than the middle of utterances. The same procedure was used as in Jusczyk & Aslin (1995), however our stimuli were contro...

Topic segmentation was addressed by a large amount of work from which it is not easy to draw conclusions, especially about the need for knowledge. In this article, we propose to combine in the same framework two methods for improving the results of a topic segmenter based on lexical reiteration. The first one is endogenous and exploits the distributional similarity of words in a document for di...

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...

ELIS, University of Ghent, Sint-Pietersnieuwstraat 41, B-9000 Ghent, Belgium martens,odul,rvparijs @elis.rug.ac.be Dept Language & Speech, University of Nijmegen, P.O. Box 9103, 6500 HD Nijmegen, The Netherlands [email protected] ESAT, K.U.Leuven, Kasteelpark Arenberg 10, B-3001 Heverlee, Belgium kris.demuynck,tom.laureys,jacques.duchateau @esat.kuleuven.ac.be Abstract This paper describe...

automatic segmentation of multiple sclerosis (ms) lesions in brain magnetic resonance imaging (mri) has been widely investigated in the recent years with the goal of helping ms diagnosis and patient follow‑up. in this research work, gaussian mixture model (gmm) has been used to segment the ms lesions in mris, including t1‑weighted (t1‑w), t2‑w, and t2‑fluid attenuation inversion recovery. usual...