Cédric Lopez


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A Dataset for Anaphora Analysis in French Emails
Hani Guenoune | Kevin Cousot | Mathieu Lafourcade | Melissa Mekaoui | Cédric Lopez
Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference

In 2019, about 293 billion emails were sent worldwide every day. They are a valuable source of information and knowledge for professionals. Since the 90’s, many studies have been done on emails and have highlighted the need for resources regarding numerous NLP tasks. Due to the lack of available resources for French, very few studies on emails have been conducted. Anaphora resolution in emails is an unexplored area, annotated resources are needed, at least to answer a first question: Does email communication have specifics that must be addressed to tackle the anaphora resolution task? In order to answer this question 1) we build a French emails corpus composed of 100 anonymized professional threads and make it available freely for scientific exploitation. 2) we provide annotations of anaphoric links in the email collection.


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Typologies pour l’annotation de textes non standard en français (Typologies for the annotation of non-standard French texts)
Louise Tarrade | Cédric Lopez | Rachel Panckhurst | Geroges Antoniadis
Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts

La tâche de normalisation automatique des messages issus de la communication électronique médiée requiert une étape préalable consistant à identifier les phénomènes linguistiques. Dans cet article, nous proposons deux typologies pour l’annotation de textes non standard en français, relevant respectivement des niveaux morpho-lexical et morpho-syntaxique. Ces typologies ont été développées en conciliant les typologies existantes et en les faisant évoluer en parallèle d’une annotation manuelle de tweets et de SMS.


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Encoding Adjective Scales for Fine-grained Resources
Cédric Lopez | Frédérique Segond | Christiane Fellbaum
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We propose an automatic approach towards determining the relative location of adjectives on a common scale based on their strength. We focus on adjectives expressing different degrees of goodness occurring in French product (perfumes) reviews. Using morphosyntactic patterns, we extract from the reviews short phrases consisting of a noun that encodes a particular aspect of the perfume and an adjective modifying that noun. We then associate each such n-gram with the corresponding product aspect and its related star rating. Next, based on the star scores, we generate adjective scales reflecting the relative strength of specific adjectives associated with a shared attribute of the product. An automatic ordering of the adjectives “correct” (correct), “sympa” (nice), “bon” (good) and “excellent” (excellent) according to their score in our resource is consistent with an intuitive scale based on human judgments. Our long-term objective is to generate different adjective scales in an empirical manner, which could allow the enrichment of lexical resources.

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Learning to Search for Recognizing Named Entities in Twitter
Ioannis Partalas | Cédric Lopez | Nadia Derbas | Ruslan Kalitvianski
Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)

We presented in this work our participation in the 2nd Named Entity Recognition for Twitter shared task. The task has been cast as a sequence labeling one and we employed a learning to search approach in order to tackle it. We also leveraged LOD for extracting rich contextual features for the named-entities. Our submission achieved F-scores of 46.16 and 60.24 for the classification and the segmentation tasks and ranked 2nd and 3rd respectively. The post-analysis showed that LOD features improved substantially the performance of our system as they counter-balance the lack of context in tweets. The shared task gave us the opportunity to test the performance of NER systems in short and noisy textual data. The results of the participated systems shows that the task is far to be considered as a solved one and methods with stellar performance in normal texts need to be revised.

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Comparing Named-Entity Recognizers in a Targeted Domain: Handcrafted Rules vs Machine Learning
Ioannis Partalas | Cédric Lopez | Frédérique Segond
Actes de la conférence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)

Comparing Named-Entity Recognizers in a Targeted Domain : Handcrafted Rules vs. Machine Learning Named-Entity Recognition concerns the classification of textual objects in a predefined set of categories such as persons, organizations, and localizations. While Named-Entity Recognition is well studied since 20 years, the application to specialized domains still poses challenges for current systems. We developed a rule-based system and two machine learning approaches to tackle the same task : recognition of product names, brand names, etc., in the domain of Cosmetics, for French. Our systems can thus be compared under ideal conditions. In this paper, we introduce both systems and we compare them.


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Un système expert fondé sur une analyse sémantique pour l’identification de menaces d’ordre biologique
Cédric Lopez | Aleksandra Ponomareva | Cécile Robin | André Bittar | Xabier Larrucea | Frédérique Segond | Marie-Hélène Metzger
Actes de la 22e conférence sur le Traitement Automatique des Langues Naturelles. Démonstrations

Le projet européen TIER (Integrated strategy for CBRN – Chemical, Biological, Radiological and Nuclear – Threat Identification and Emergency Response) vise à intégrer une stratégie complète et intégrée pour la réponse d’urgence dans un contexte de dangers biologiques, chimiques, radiologiques, nucléaires, ou liés aux explosifs, basée sur l’identification des menaces et d’évaluation des risques. Dans cet article, nous nous focalisons sur les risques biologiques. Nous présentons notre système expert fondé sur une analyse sémantique, permettant l’extraction de données structurées à partir de données non structurées dans le but de raisonner.


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Generating a Resource for Products and Brandnames Recognition. Application to the Cosmetic Domain.
Cédric Lopez | Frédérique Segond | Olivier Hondermarck | Paolo Curtoni | Luca Dini
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Named Entity Recognition task needs high-quality and large-scale resources. In this paper, we present RENCO, a based-rules system focused on the recognition of entities in the Cosmetic domain (brandnames, product names, …). RENCO has two main objectives: 1) Generating resources for named entity recognition; 2) Mining new named entities relying on the previous generated resources. In order to build lexical resources for the cosmetic domain, we propose a system based on local lexico-syntactic rules complemented by a learning module. As the outcome of the system, we generate both a simple lexicon and a structured lexicon. Results of the evaluation show that even if RENCO outperforms a classic Conditional Random Fields algorithm, both systems should combine their respective strengths.

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Towards Electronic SMS Dictionary Construction: An Alignment-based Approach
Cédric Lopez | Reda Bestandji | Mathieu Roche | Rachel Panckhurst
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper, we propose a method for aligning text messages (entitled AlignSMS) in order to automatically build an SMS dictionary. An extract of 100 text messages from the 88milSMS corpus (Panckhurst el al., 2013, 2014) was used as an initial test. More than 90,000 authentic text messages in French were collected from the general public by a group of academics in the south of France in the context of the sud4science project (http://www.sud4science.org). This project is itself part of a vast international SMS data collection project, entitled sms4science (http://www.sms4science.org, Fairon et al. 2006, Cougnon, 2014). After corpus collation, pre-processing and anonymisation (Accorsi et al., 2012, Patel et al., 2013), we discuss how “raw” anonymised text messages can be transcoded into normalised text messages, using a statistical alignment method. The future objective is to set up a hybrid (symbolic/statistic) approach based on both grammar rules and our statistical AlignSMS method.


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NOMIT: Automatic Titling by Nominalizing
Cédric Lopez | Violaine Prince | Mathieu Roche
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Just Title It! (by an Online Application)
Cédric Lopez | Violaine Prince | Mathieu Roche
Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics


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Automatic titling of Articles Using Position and Statistical Information
Cédric Lopez | Violaine Prince | Mathieu Roche
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011