Influence des domaines de spécialité dans l'extraction de termes-clés

National audienceLes termes-clés sont les mots ou les expressions polylexicales qui représentent le contenu principal d'un document. Ils sont utiles pour diverses applications, telles que l'indexation automatique ou le résumé automatique, mais ne sont pas toujours disponibles. De ce fait, nous nous intéressons à l'extraction automatique de termes-clés et, plus particulièrement, à la difficulté de cette tâche lors du traitement de documents appartenant à certaines disciplines scientifiques. Au moyen de cinq corpus représentant cinq disciplines différentes (archéologie, linguistique, sciences de l'information, psychologie et chimie), nous déduisons une échelle de difficulté disciplinaire et analysons les facteurs qui influent sur cette difficulté

Automatic Validation of Terminology by Means of Formal Concept Analysis

International audienceTerm extraction tools extract candidate terms and annotate their occurrences in the texts. However, not all these occurrences are terminological and, at present, this is still a very challenging issue to distinguish when a candidate term is really used with a termino-logical meaning. The validation of term annotations is presented as a bi-classification model that classifies each term occurrence as a termi-nological or non-terminological occurrence. A context-based hypothesis approach is applied to a training corpus: we assume that the words in the sentence which contains the studied occurrence can be used to build positive and negative hypotheses that are further used to classify unde-termined examples. The method is applied and evaluated on a french corpus in the linguistic domain and we also mention some improvements suggested by a quantitative and qualitative evaluation

Identification, alignement, et traductions des adjectifs relationnels en corpus comparables

National audienceRÉSUMÉ Dans cet article, nous extrayons des adjectifs relationnels français et nous les alignons automatiquement avec les noms dont ils sont dérivés en utilisant un corpus monolingue. Les alignements adjectif-nom seront ensuite utilisés dans la traduction compositionelle des termes complexes de la forme [N AdjR] à partir d'un corpus comparable français-anglais. Un nouveau terme [N N ï¿¿ ] (ex. cancer du poumon) sera obtenu en remplaçant l'adjectif relationnel Ad jR (ex. pulmonaire) dans [N AdjR] (ex. cancer pulmonaire) par le nom N ï¿¿ (ex. poumon) avec lequel il est aligné. Si aucune traduction n'est proposée pour [N AdjR], nous considérons que ses traduction(s) sont équivalentes à celle(s) de sa paraphrase [N N ï¿¿ ]. Nous expérimentons avec un corpus comparable dans le domaine de cancer du sein, et nous obtenons des alignements adjectif-nom qui aident à traduire des termes complexes de la forme [N AdjR] vers l'anglais avec une précision de 86 %. ABSTRACT Identification, Alignment, and Tranlsation of Relational Adjectives from Comparable Corpora In this paper, we extract French relational adjectives and automatically align them with the nouns they are derived from by using a monolingual corpus. The obtained adjective-noun alignments are then used in the compositional translation of compound nouns of the form [N ADJR] with a French-English comparable corpora. A new term [N N ï¿¿ ] (eg, cancer du poumon) is obtained by replacing the relational adjective Ad jR (eg, pulmonaire) in [N AdjR] (eg, cancer pulmonaire) by its corresponding N ï¿¿ (eg, poumon). If no translation(s) are obtained for [N AdjR], we consider the one(s) obtained for its paraphrase [N N ï¿¿ ]. We experiment with a comparable corpora in the field of breast cancer, and we get adjective-noun alignments that help in translating French compound nouns of the form [N AdjR] to English with a precision of 86%

Uticaj klasifikacije teksta na primene u obradi prirodnih jezika

The main goal of this dissertation is to put different text classification tasks in the same frame, by mapping the input data into the common vector space of linguistic attributes. Subsequently, several classification problems of great importance for natural language processing are solved by applying the appropriate classification algorithms. The dissertation deals with the problem of validation of bilingual translation pairs, so that the final goal is to construct a classifier which provides a substitute for human evaluation and which decides whether the pair is a proper translation between the appropriate languages by means of applying a variety of linguistic information and methods. In dictionaries it is useful to have a sentence that demonstrates use for a particular dictionary entry. This task is called the classification of good dictionary examples. In this thesis, a method is developed which automatically estimates whether an example is good or bad for a specific dictionary entry. Two cases of short message classification are also discussed in this dissertation. In the first case, classes are the authors of the messages, and the task is to assign each message to its author from that fixed set. This task is called authorship identification. The other observed classification of short messages is called opinion mining, or sentiment analysis. Starting from the assumption that a short message carries a positive or negative attitude about a thing, or is purely informative, classes can be: positive, negative and neutral. These tasks are of great importance in the field of natural language processing and the proposed solutions are language-independent, based on machine learning methods: support vector machines, decision trees and gradient boosting. For all of these tasks, a demonstration of the effectiveness of the proposed methods is shown on for the Serbian language.Osnovni cilj disertacije je stavljanje različitih zadataka klasifikacije teksta u isti okvir, preslikavanjem ulaznih podataka u isti vektorski prostor lingvističkih atributa..

SEMANTIQUE DISTRIBUTIONNELLE

This special issue contains state-of-the-art papers on distributional semantic