This paper presents an overview of automatic methods for building domain knowledge
structures (domain models) from text collections. Applications of domain models have a long …

The continuous sophistication in clinical informationprocessing motivates the development
of a dictionary likeWordNet for Medical Events in order to convey the valuableinformation …

Financial documents play an important role in modern financial analysis and information
retrieval tasks. In order to accomplish various investigational needs, financial organizations …

In this paper, we propose a novel way to include unsupervised feature selection methods in
probabilistic taxonomy learning models. We leverage on the computation of logistic …

Capturing word meaning is one of the challenges of natural language processing (NLP).
Formal models of meaning, such as networks of words or concepts, are knowledge …

In this paper we present the Semantic Turkey Ontology Learner (ST-OL), an incremental
ontology learning system, that follows two main ideas:(1) putting final users in the learning …

Interrogation à base d’annotation sémantique Page 1 Interrogation à base d’annotation
sémantique LATRECHE Abdelkrim1, LEHIRECHE Ahmed1, BENYAHIA Kadda1 1Laboratoire …

This paper describes a range of experiments using empirical methods to adapt the WordNet
noun ontology for specific use in the biomedical domain. Our basic technique is to extract …

Ontology plays a crucial role in the growth of semantic web by providing a domain-
knowledge resource comprehensible to both humans as well as computers. Knowledge …

Modern search engines employ a number of interactive features to assist users in exploring
the document collection and expressing their information needs. Providing these features …