Davide Buscaldi


2020

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Calcul de similarité entre phrases : quelles mesures et quels descripteurs ? (Sentence Similarity : a study on similarity metrics with words and character strings )
Davide Buscaldi | Ghazi Felhi | Dhaou Ghoul | Joseph Le Roux | Gaël Lejeune | Xudong Zhang
Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Atelier DÉfi Fouille de Textes

Cet article présente notre participation à l’édition 2020 du Défi Fouille de Textes DEFT 2020 et plus précisément aux deux tâches ayant trait à la similarité entre phrases. Dans notre travail nous nous sommes intéressé à deux questions : celle du choix de la mesure du similarité d’une part et celle du choix des opérandes sur lesquelles se porte la mesure de similarité. Nous avons notamment étudié la question de savoir s’il fallait utiliser des mots ou des chaînes de caractères (mots ou non-mots). Nous montrons d’une part que la similarité de Bray-Curtis peut être plus efficace et surtout plus stable que la similarité cosinus et d’autre part que le calcul de similarité sur des chaînes de caractères est plus efficace que le même calcul sur des mots.

2019

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Indexation et appariements de documents cliniques pour le Deft 2019 (Indexing and pairing texts of the medical domain )
Davide Buscaldi | Dhaou Ghoul | Joseph Le Roux | Gaël Lejeune
Actes de la Conférence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Défi Fouille de Textes (atelier TALN-RECITAL)

Dans cet article, nous présentons nos méthodes pour les tâches d’indexation et d’appariements du Défi Fouile de Textes (Deft) 2019. Pour la taĉhe d’indexation nous avons testé deux méthodes, une fondée sur l’appariemetn préalable des documents du jeu de tset avec les documents du jeu d’entraînement et une autre méthode fondée sur l’annotation terminologique. Ces méthodes ont malheureusement offert des résultats assez faible. Pour la tâche d’appariement, nous avons dévellopé une méthode sans apprentissage fondée sur des similarités de chaînes de caractères ainsi qu’une méthode exploitant des réseaux siamois. Là encore les résultats ont été plutôt décevant même si la méthode non supervisée atteint un score plutôt honorable pour une méthode non-supervisée : 62% .

2018

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Apport des dépendances syntaxiques et des patrons séquentiels à l’extraction de relations ()
Kata Gábor | Nadège Lechevrel | Isabelle Tellier | Davide Buscaldi | Haifa Zargayouna | Thierry Charnois
Actes de la Conférence TALN. Volume 1 - Articles longs, articles courts de TALN

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Modèles en Caractères pour la Détection de Polarité dans les Tweets (Character-level Models for Polarity Detection in Tweets )
Davide Buscaldi | Joseph Le Roux | Gaël Lejeune
Actes de la Conférence TALN. Volume 2 - Démonstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT

Dans cet article, nous présentons notre contribution au Défi Fouille de Textes 2018 au travers de trois méthodes originales pour la classification thématique et la détection de polarité dans des tweets en français. Nous y avons ajouté un système de vote. Notre première méthode est fondée sur des lexiques (mots et emojis), les n-grammes de caractères et un classificateur à vaste marge (ou SVM). tandis que les deux autres sont des méthodes endogènes fondées sur l’extraction de caractéristiques au grain caractères : un modèle à mémoire à court-terme persistante (ou BiLSTM pour Bidirectionnal Long Short-Term Memory) et perceptron multi-couche d’une part et un modèle de séquences de caractères fermées fréquentes et classificateur SVM d’autre part. Le BiLSTM a produit de loin les meilleurs résultats puisqu’il a obtenu la première place sur la tâche 1, classification binaire de tweets selon qu’ils traitent ou non des transports, et la troisième place sur la tâche 2, classification de la polarité en 4 classes. Ce résultat est d’autant plus intéressant que la méthode proposée est faiblement paramétrique, totalement endogène et qu’elle n’implique aucun pré-traitement.

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SemEval-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers
Kata Gábor | Davide Buscaldi | Anne-Kathrin Schumann | Behrang QasemiZadeh | Haïfa Zargayouna | Thierry Charnois
Proceedings of The 12th International Workshop on Semantic Evaluation

This paper describes the first task on semantic relation extraction and classification in scientific paper abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations and includes three different subtasks. The subtasks were designed so as to compare and quantify the effect of different pre-processing steps on the relation classification results. We expect the task to be relevant for a broad range of researchers working on extracting specialized knowledge from domain corpora, for example but not limited to scientific or bio-medical information extraction. The task attracted a total of 32 participants, with 158 submissions across different scenarios.

2017

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LIPN-UAM at EmoInt-2017:Combination of Lexicon-based features and Sentence-level Vector Representations for Emotion Intensity Determination
Davide Buscaldi | Belem Priego
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

This paper presents the combined LIPN-UAM participation in the WASSA 2017 Shared Task on Emotion Intensity. In particular, the paper provides some highlights on the Tweetaneuse system that was presented to the shared task. We combined lexicon-based features with sentence-level vector representations to implement a random forest regressor.

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LIPN at SemEval-2017 Task 10: Filtering Candidate Keyphrases from Scientific Publications with Part-of-Speech Tag Sequences to Train a Sequence Labeling Model
Simon David Hernandez | Davide Buscaldi | Thierry Charnois
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes the system used by the team LIPN in SemEval 2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications. The team participated in Scenario 1, that includes three subtasks, Identification of keyphrases (Subtask A), Classification of identified keyphrases (Subtask B) and Extraction of relationships between two identified keyphrases (Subtask C). The presented system was mainly focused on the use of part-of-speech tag sequences to filter candidate keyphrases for Subtask A. Subtasks A and B were addressed as a sequence labeling problem using Conditional Random Fields (CRFs) and even though Subtask C was out of the scope of this approach, one rule was included to identify synonyms.

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Exploring Vector Spaces for Semantic Relations
Kata Gábor | Haïfa Zargayouna | Isabelle Tellier | Davide Buscaldi | Thierry Charnois
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Word embeddings are used with success for a variety of tasks involving lexical semantic similarities between individual words. Using unsupervised methods and just cosine similarity, encouraging results were obtained for analogical similarities. In this paper, we explore the potential of pre-trained word embeddings to identify generic types of semantic relations in an unsupervised experiment. We propose a new relational similarity measure based on the combination of word2vec’s CBOW input and output vectors which outperforms concurrent vector representations, when used for unsupervised clustering on SemEval 2010 Relation Classification data.

2016

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Détection et classification non supervisées de relations sémantiques dans des articles scientifiques (Unsupervised Classification of Semantic Relations in Scientific Papers)
Kata Gábor | Isabelle Tellier | Thierry Charnois | Haïfa Zargayouna | Davide Buscaldi
Actes de la conférence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)

Dans cet article, nous abordons une tâche encore peu explorée, consistant à extraire automatiquement l’état de l’art d’un domaine scientifique à partir de l’analyse d’articles de ce domaine. Nous la ramenons à deux sous-tâches élémentaires : l’identification de concepts et la reconnaissance de relations entre ces concepts. Une extraction terminologique permet d’identifier les concepts candidats, qui sont ensuite alignés à des ressources externes. Dans un deuxième temps, nous cherchons à reconnaître et classifier automatiquement les relations sémantiques entre concepts de manière nonsupervisée, en nous appuyant sur différentes techniques de clustering et de biclustering. Nous mettons en œuvre ces deux étapes dans un corpus extrait de l’archive de l’ACL Anthology. Une analyse manuelle nous a permis de proposer une typologie des relations sémantiques, et de classifier un échantillon d’instances de relations. Les premières évaluations suggèrent l’intérêt du biclustering pour détecter de nouveaux types de relations dans le corpus.

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Semantic Annotation of the ACL Anthology Corpus for the Automatic Analysis of Scientific Literature
Kata Gábor | Haïfa Zargayouna | Davide Buscaldi | Isabelle Tellier | Thierry Charnois
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper describes the process of creating a corpus annotated for concepts and semantic relations in the scientific domain. A part of the ACL Anthology Corpus was selected for annotation, but the annotation process itself is not specific to the computational linguistics domain and could be applied to any scientific corpora. Concepts were identified and annotated fully automatically, based on a combination of terminology extraction and available ontological resources. A typology of semantic relations between concepts is also proposed. This typology, consisting of 18 domain-specific and 3 generic relations, is the result of a corpus-based investigation of the text sequences occurring between concepts in sentences. A sample of 500 abstracts from the corpus is currently being manually annotated with these semantic relations. Only explicit relations are taken into account, so that the data could serve to train or evaluate pattern-based semantic relation classification systems.

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LIPN-IIMAS at SemEval-2016 Task 1: Random Forest Regression Experiments on Align-and-Differentiate and Word Embeddings penalizing strategies
Oscar William Lightgow Serrano | Ivan Vladimir Meza Ruiz | Albert Manuel Orozco Camacho | Jorge Garcia Flores | Davide Buscaldi
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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SOPA: Random Forests Regression for the Semantic Textual Similarity task
Davide Buscaldi | Jorge García Flores | Ivan V. Meza | Isaac Rodríguez
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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QASSIT: A Pretopological Framework for the Automatic Construction of Lexical Taxonomies from Raw Texts
Guillaume Cleuziou | Davide Buscaldi | Gael Dias | Vincent Levorato | Christine Largeron
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

2014

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LIPN: Introducing a new Geographical Context Similarity Measure and a Statistical Similarity Measure based on the Bhattacharyya coefficient
Davide Buscaldi | Jorge García Flores | Joseph Le Roux | Nadi Tomeh | Belém Priego Sanchez
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

2013

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LIPN-CORE: Semantic Text Similarity using n-grams, WordNet, Syntactic Analysis, ESA and Information Retrieval based Features
Davide Buscaldi | Joseph Le Roux | Jorge J. García Flores | Adrian Popescu
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity

2012

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IRIT: Textual Similarity Combining Conceptual Similarity with an N-Gram Comparison Method
Davide Buscaldi | Ronan Tournier | Nathalie Aussenac-Gilles | Josiane Mothe
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

2010

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Evaluation Protocol and Tools for Question-Answering on Speech Transcripts
Nicolas Moreau | Olivier Hamon | Djamel Mostefa | Sophie Rosset | Olivier Galibert | Lori Lamel | Jordi Turmo | Pere R. Comas | Paolo Rosso | Davide Buscaldi | Khalid Choukri
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Question Answering (QA) technology aims at providing relevant answers to natural language questions. Most Question Answering research has focused on mining document collections containing written texts to answer written questions. In addition to written sources, a large (and growing) amount of potentially interesting information appears in spoken documents, such as broadcast news, speeches, seminars, meetings or telephone conversations. The QAST track (Question-Answering on Speech Transcripts) was introduced in CLEF to investigate the problem of question answering in such audio documents. This paper describes in detail the evaluation protocol and tools designed and developed for the CLEF-QAST evaluation campaigns that have taken place between 2007 and 2009. We first remind the data, question sets, and submission procedures that were produced or set up during these three campaigns. As for the evaluation procedure, the interface that was developed to ease the assessors’ work is described. In addition, this paper introduces a methodology for a semi-automatic evaluation of QAST systems based on time slot comparisons. Finally, the QAST Evaluation Package 2007-2009 resulting from these evaluation campaigns is also introduced.

2008

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Geo-WordNet: Automatic Georeferencing of WordNet
Davide Buscaldi | Paolo Rosso
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

WordNet has been used extensively as a resource for the Word Sense Disambiguation (WSD) task, both as a sense inventory and a repository of semantic relationships. Recently, we investigated the possibility to use it as a resource for the Geographical Information Retrieval task, more specifically for the toponym disambiguation task, which could be considered a specialization of WSD. We found that it would be very useful to assign to geographical entities inWordNet their coordinates, especially in order to implement geometric shapebased disambiguation methods. This paper presents Geo-WordNet, an automatic annotation of WordNet with geographical coordinates. The annotation has been carried out by extracting geographical synsets from WordNet, together with their holonyms and hypernyms, and comparing them to the entries in the Wikipedia-World geographical database. A weight was calculated for each of the candidate annotations, on the basis of matches found between the database entries and synset gloss, holonyms and hypernyms. The resulting resource may be used in Geographical Information Retrieval related tasks, especially for toponym disambiguation.

2007

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UPV-WSD : Combining different WSD Methods by means of Fuzzy Borda Voting
Davide Buscaldi | Paolo Rosso
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)

2006

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Mining Knowledge fromWikipedia for the Question Answering task
Davide Buscaldi | Paolo Rosso
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

Although significant advances have been made recently in the Question Answering technology, more steps have to be undertaken in order to obtain better results. Moreover, the best systems at the CLEF and TREC evaluation exercises are very complex systems based on custom-built, expensive ontologies whose aim is to provide the systems with encyclopedic knowledge. In this paper we investigated the use of Wikipedia, the open domain encyclopedia, for the Question Answering task. Previous works considered Wikipedia as a resource where to look for the answers to the questions. We focused on some different aspects of the problem, such as the validation of the answers as returned by our Question Answering System and on the use of Wikipedia “categories” in order to determine a set of patterns that should fit with the expected answer. Validation consists in, given a possible answer, saying wether it is the right one or not. The possibility to exploit the categories ofWikipedia was not considered until now. We performed our experiments using the Spanish version of Wikipedia, with the set of questions of the last CLEF Spanish monolingual exercise. Results show that Wikipedia is a potentially useful resource for the Question Answering task.

2004

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The upv-unige-CIAOSENSO WSD system
Davide Buscaldi | Paolo Rosso | Francesco Masulli
Proceedings of SENSEVAL-3, the Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text