Clustering and Relational Ambiguity: from Text Data to Natural Data

Text data is often seen as "take-away" materials with little noise and easy to process information. Main questions are how to get data and transform them into a good document format. But data can be sensitive to noise oftenly called ambiguities. Ambiguities are aware from a long time, mainly because polysemy is obvious in language and context is required to remove uncertainty. I claim in this paper that syntactic context is not suffisant to improve interpretation. In this paper I try to explain that firstly noise can come from natural data themselves, even involving high technology, secondly texts, seen as verified but meaningless, can spoil content of a corpus; it may lead to contradictions and background noise

Open Data Platform for Knowledge Access in Plant Health Domain : VESPA Mining

Important data are locked in ancient literature. It would be uneconomic to produce these data again and today or to extract them without the help of text mining technologies. Vespa is a text mining project whose aim is to extract data on pest and crops interactions, to model and predict attacks on crops, and to reduce the use of pesticides. A few attempts proposed an agricultural information access. Another originality of our work is to parse documents with a dependency of the document architecture

Apprentissage d'un ensemble pré-structuré de concepts d'un domaine : l'outil Galex

The huge amount of electronic textual information increases exponentially just as easily as archives and working documents in academic organizations, in administration and in firms. A solution for structuring this mountain of textual database is to build a knowledge model to index this information. One way can be obtained by data extraction and classification producing conceptual indexing by knowledge acquisition. Traditionally the classification methods of Data Analysis were adapted while used for the classical table of data under an object/characteristics/value format. We present Galex (Graph Analyzer for LEXicometry) which develops structuration of knowledge by a term clustering method. This structuration synthetizes the content of information providing the mapping data to information filtering or hypertextual navigation on similar documents. Galex aims at taking into account the nature of the data to which it is applied : natural language. The complexity of natural language is well known: sense ambiguity, multiple grammatical construction of sentence, style, term creationáWe show through integration of poorly defined, though useful as concept, ontology, term and corpus, notions that clustering can be improved by adding linguistic knowledge. We base our approach on typical phenomena such as graph-statistical relations between terms, scheme relations in a context and canonical reduction of variants.La quantité d'information textuelle augmente de façon exponentielle aussi bien comme archives que documents de travail dans les organisations académiques, dans les administrations et dans les entreprises. Une solution pour structurer cette montagne de données textuelles est de construire un modèle de connaissances pour indexer cette information. L'acquisition de connaissances doit permettre d'extraire et classifier les données pour aboutir à une indexation conceptuelle. Traditionnellement les méthodes de classification d'analyse de données étaient adaptées pour des tables classiques de données de la forme objet/attribut/valeur. Nous présentons Galex (Graph Analyzer for LEXicometry) qui développe une structuration de la connaissance grâce à une méthode de clustering de termes. Cette structuration a pour but de synthétiser le contenu d'information présentant un intérêt majeur dans des applications de filtrage d'information ou de navigation hypertextuelle sur des documents similaires. Galex prend en compte la nature des données sur lesquelles il s'applique : le langage naturel. La complexité du langage naturel est bien connue : ambiguïté de sens, constructions grammaticales multiples de la phrase, style, création de termesá Nous montrons qu'à travers l'intégration de notions mal définies mais utiles telles que "concept", "ontologie" et "corpus", le clustering peut être amélioré par adjonctions de connaissances linguistiques. Nous basons notre approche sur des phénomènes typiques tels que des relations graphe-statistiques entre termes, des relations de schéma dans un contexte et la réduction canonique de formes variantes