This paper presents the computational aspects of the SemComp project, a multidisciplinary collaboration aiming at observing how interacting with documents acts on knowledge acquisition. It is based on a model for personalized semantic resources inspired from componential linguistics. The paper describes the advances in both the computational model's definition as well as its implementation in a Web oriented application. Functionalities and technical choices are presented with regards to the expected experiments.
Normalization is involved in many fields of information processing. It improves the performance of several applications, such as information retrieval or information extraction, and makes the construction of language resources more reliable. Normalization consists in standardizing each variant of a term or named entity into a unique form, and in this way restricts the impact of language variation. Our work applies to named entity normalization, and aims at optimizing fine-grained corpus analyses carried out by the TecKnowMetrix Company. Our approach mixes several methods, such as pattern matching, similarity metrics and endogenous techniques. Moreover, we place the user in the center of our normalization process, in order to obtain fully reliable data that fit his or her needs.
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