La glose comme outil de désambiguïsation référentielle des noms propres purs

Vicente, Montserrat Rangel

doi:10.4000/corela.1212

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…The recognition of entities is an important task in NLP and according to Vicente [3], named entity is defined as a concept used to designate a mono-referential discursive element, which coincides with a proper name definition and follows specific syntactic patterns. The 'reference' refers to the link between the linguistic expression and a single element of the world (i.e., the referent).…”

Section: Introductionmentioning

confidence: 99%

Comparing supervised learning algorithms for Spatial Nominal Entity recognition

Medad¹,

Gaio²,

Moncla³

et al. 2020

AGILE GIScience Ser.

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Abstract. Discourse may contain both named and nominal entities. Most common nouns or nominal mentions in natural language do not have a single, simple meaning but rather a number of related meanings. This form of ambiguity led to the development of a task in natural language processing known as Word Sense Disambiguation. Recognition and categorisation of named and nominal entities is an essential step for Word Sense Disambiguation methods. Up to now, named entity recognition and categorisation systems mainly focused on the annotation, categorisation and identification of named entities. This paper focuses on the annotation and the identification of spatial nominal entities. We explore the combination of Transfer Learning principle and supervised learning algorithms, in order to build a system to detect spatial nominal entities. For this purpose, different supervised learning algorithms are evaluated with three different context sizes on two manually annotated datasets built from Wikipedia articles and hiking description texts. The studied algorithms have been selected for one or more of their specific properties potentially useful in solving our problem. The results of the first phase of experiments reveal that the selected algorithms have similar performances in terms of ability to detect spatial nominal entities. The study also confirms the importance of the size of the window to describe the context, when word-embedding principle is used to represent the semantics of each word.

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Section: Introductionmentioning

confidence: 99%

Comparing supervised learning algorithms for Spatial Nominal Entity recognition

Medad¹,

Gaio²,

Moncla³

et al. 2020

AGILE GIScience Ser.

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“…Pour cela, il faut au préalable identifier toutes les formes linguistiques, qui, à l'instar des noms propres, désignent de manière univoque une entité (Dupont et al, 2002). Comme précisé dans (Vicente, 2005), l'EN est la notion utilisée en TAL pour désigner les éléments discursifs mono-référentiels qui coïncident en partie avec les noms propres et qui suivent des patrons syntaxiques déterminés. Ainsi l'approche ici proposée apporte une réponse à ces préalables.…”

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Services Web pour l’annotation sémantique d’information spatiale à partir de corpus textuels

Moncla

Gaio

2018

Rev. Int. Geomat.

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L'annotation sémantique d'information spatiale a pour objectif de repérer des mots ou des syntagmes décrivant des références géographiques (noms de lieux) ainsi que diverses expressions spatiales associées. L'une des plus importantes difficultés pour concevoir un système automatique d'annotation d'un tel type d'information est due aux ambiguïtés liées aux entités spatiales. Une approche modulaire basée sur des services Web a été choisie. La méthodologie proposée repose sur la combinaison d'une étape de pré-traitement (analyse morphosyntaxique), d'une cascade de transducteurs, et d'une étape de classification utilisant des ressources du Web des données. Un avantage de cette approche est la possibilité d'obtenir des traitements partiels ou encore de mettre en concurrence certains modules réalisant la même tâche. ABSTRACT. The semantic annotation of spatial information aims to identify words or phrases describing geographical references (place names) as well as various associated spatial expressions. One of the major difficulties in designing an automatic annotation system for such information is due to ambiguities related to spatial entities. A modular approach based on Web services was chosen. The proposed methodology is based on the combination of a pre-processing step using external morphosyntactic analysers, a cascade of transducers, and a classification step using linked data. An advantage of this approach is the possibility to obtain partial processing or to evaluate competing methods doing the same task.

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2016

Named Entities for Computational Linguistics

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La glose comme outil de désambiguïsation référentielle des noms propres purs

Abstract: Ce document a été généré automatiquement le 20 avril 2019. Corela-cognition, représentation, langage est mis à disposition selon les termes de la licence Creative Commons Attribution-Pas d'Utilisation Commerciale-Partage dans les Mêmes Conditions 4.0 International.

Cited by 6 publications

References 4 publications

Comparing supervised learning algorithms for Spatial Nominal Entity recognition

Comparing supervised learning algorithms for Spatial Nominal Entity recognition

Services Web pour l’annotation sémantique d’information spatiale à partir de corpus textuels

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