SemEval-2016 Task 13: Taxonomy Extraction Evaluation (TExEval-2)

This paper describes the second edition of the shared task on Taxonomy Extraction Evaluation organised as part of SemEval 2016. This task aims to extract hypernym-hyponym relations between a given list of domain-specific terms and then to construct a domain taxonomy based on them. TExEval-2 introduced a multilingual setting for this task, covering four different languages including English, Dutch, Italian and French from domains as diverse as environment, food and science. A total of 62 runs submitted by 5 different teams were evaluated using structural measures, by comparison with gold standard taxonomies and by manual quality assessment of novel relations.Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 (INSIGHT

Learning Pretopological Spaces for Lexical Taxonomy Acquisition

International audienceIn this paper, we propose a new methodology for semi-supervised acquisition of lexical taxonomies from a list of existing terms. Our approach is based on the theory of pretopology that offers a powerful formalism to model semantic relations and transform a list of terms into a structured term space by combining different discriminant criteria. In order to learn a parameterized pretopological space, we define the Learning Pretopological Spaces strategy based on genetic algorithms. The rare but accurate pieces of knowledge given by an expert (semi-supervision) or automatically extracted with existing linguistic patterns (auto-supervision) are used to parameterize the different features defining the pretopological term space. Then, a structuring algorithm is used to transform the pretopological space into a lexical taxonomy, i.e. a direct acyclic graph. Results over three standard datasets (two from WordNet and one from UMLS) evidence improved performances against existing associative and pattern-based state-of-the-art approaches

A supervised approach to taxonomy extraction using word embeddings

Large collections of texts are commonly generated by large organizations and making sense of these collections of texts is a significant challenge. One method for handling this is to organize the concepts into a hierarchical structure such that similar concepts can be discovered and easily browsed. This approach was the subject of a recent evaluation campaign, TExEval, however the results of this task showed that none of the systems consistently outperformed a relatively simple baseline.In order to solve this issue, we propose a new method that uses supervised learning to combine multiple features with a support vector machine classifier including the baseline features. We show that this outperforms the baseline and thus provides a stronger method for identifying taxonomic relations than previous method

Fuzzy Sets, Fuzzy Logic and Their Applications

The present book contains 20 articles collected from amongst the 53 total submitted manuscripts for the Special Issue “Fuzzy Sets, Fuzzy Loigic and Their Applications” of the MDPI journal Mathematics. The articles, which appear in the book in the series in which they were accepted, published in Volumes 7 (2019) and 8 (2020) of the journal, cover a wide range of topics connected to the theory and applications of fuzzy systems and their extensions and generalizations. This range includes, among others, management of the uncertainty in a fuzzy environment; fuzzy assessment methods of human-machine performance; fuzzy graphs; fuzzy topological and convergence spaces; bipolar fuzzy relations; type-2 fuzzy; and intuitionistic, interval-valued, complex, picture, and Pythagorean fuzzy sets, soft sets and algebras, etc. The applications presented are oriented to finance, fuzzy analytic hierarchy, green supply chain industries, smart health practice, and hotel selection. This wide range of topics makes the book interesting for all those working in the wider area of Fuzzy sets and systems and of fuzzy logic and for those who have the proper mathematical background who wish to become familiar with recent advances in fuzzy mathematics, which has entered to almost all sectors of human life and activity

Approximation to the theory of affinities to manage the problems of the groupping process

New economic and enterprise needs have increased the interest and utility of the methods of the grouping process based on the theory of uncertainty. A fuzzy grouping (clustering) process is a key phase of knowledge acquisition and reduction complexity regarding different groups of objects. Here, we considered some elements of the theory of affinities and uncertain pretopology that form a significant support tool for a fuzzy clustering process. A Galois lattice is introduced in order to provide a clearer vision of the results. We made an homogeneous grouping process of the economic regions of Russian Federation and Ukraine. The obtained results gave us a large panorama of a regional economic situation of two countries as well as the key guidelines for the decision-making. The mathematical method is very sensible to any changes the regional economy can have. We gave an alternative method of the grouping process under uncertainty

Structuration de données par apprentissage non-supervisé : applications aux données textuelles

En fouille de données, le succès d'une méthode tient au fait qu'elle permet de répondre par un algorithme intuitif à un besoin pratique bien théorisé. C'est avec cet éclairage que nous présentons un ensemble de contributions, élaborées durant ces dix dernières années, et répondant au besoin pratique de structurer automatiquement un ensemble de données. Dans un premier temps nous proposons de nouveaux modèles théoriques de structuration complexe en classes d’individus ; il s’agit alors d'extraire automatiquement d'un ensemble de données, des structures de classification plus proches de leur organisation réelle telle qu’observée (classification recouvrante, formes symétriques), de rendre ces structures à la fois robustes (tolérance au bruit) et manipulables par l’homme (visualisation, paramétrage) et enfin d’être en mesure de les expliquer (sémantique des classes). Dans un second temps nous nous intéressons aux données textuelles via la mise en oeuvre de modèles rendant compte de la structure thématique d’une collection de textes courts dans un contexte de recherche d’information ; enfin, nous présentons un méta-modèle permettant d’apprendre automatiquement un modèle de structuration sémantique d’un ensemble de termes