Erwan Moreau


2020

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An Evaluation Method for Diachronic Word Sense Induction
Ashjan Alsulaimani | Erwan Moreau | Carl Vogel
Findings of the Association for Computational Linguistics: EMNLP 2020

The task of Diachronic Word Sense Induction (DWSI) aims to identify the meaning of words from their context, taking the temporal dimension into account. In this paper we propose an evaluation method based on large-scale time-stamped annotated biomedical data, and a range of evaluation measures suited to the task. The approach is applied to two recent DWSI systems, thus demonstrating its relevance and providing an in-depth analysis of the models.

2018

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Multilingual Word Segmentation: Training Many Language-Specific Tokenizers Smoothly Thanks to the Universal Dependencies Corpus
Erwan Moreau | Carl Vogel
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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CRF-Seq and CRF-DepTree at PARSEME Shared Task 2018: Detecting Verbal MWEs using Sequential and Dependency-Based Approaches
Erwan Moreau | Ashjan Alsulaimani | Alfredo Maldonado | Carl Vogel
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)

This paper describes two systems for detecting Verbal Multiword Expressions (VMWEs) which both competed in the closed track at the PARSEME VMWE Shared Task 2018. CRF-DepTree-categs implements an approach based on the dependency tree, intended to exploit the syntactic and semantic relations between tokens; CRF-Seq-nocategs implements a robust sequential method which requires only lemmas and morphosyntactic tags. Both systems ranked in the top half of the ranking, the latter ranking second for token-based evaluation. The code for both systems is published under the GNU General Public License version 3.0 and is available at http://github.com/erwanm/adapt-vmwe18.

2017

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Detection of Verbal Multi-Word Expressions via Conditional Random Fields with Syntactic Dependency Features and Semantic Re-Ranking
Alfredo Maldonado | Lifeng Han | Erwan Moreau | Ashjan Alsulaimani | Koel Dutta Chowdhury | Carl Vogel | Qun Liu
Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)

A description of a system for identifying Verbal Multi-Word Expressions (VMWEs) in running text is presented. The system mainly exploits universal syntactic dependency features through a Conditional Random Fields (CRF) sequence model. The system competed in the Closed Track at the PARSEME VMWE Shared Task 2017, ranking 2nd place in most languages on full VMWE-based evaluation and 1st in three languages on token-based evaluation. In addition, this paper presents an option to re-rank the 10 best CRF-predicted sequences via semantic vectors, boosting its scores above other systems in the competition. We also show that all systems in the competition would struggle to beat a simple lookup baseline system and argue for a more purpose-specific evaluation scheme.

2014

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Limitations of MT Quality Estimation Supervised Systems: The Tails Prediction Problem
Erwan Moreau | Carl Vogel
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2013

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An Approach Using Style Classification Features for Quality Estimation
Erwan Moreau | Raphael Rubino
Proceedings of the Eighth Workshop on Statistical Machine Translation

2012

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A Naive Bayes classifier for automatic correction of preposition and determiner errors in ESL text
Gerard Lynch | Erwan Moreau | Carl Vogel
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP

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Quality Estimation: an experimental study using unsupervised similarity measures
Erwan Moreau | Carl Vogel
Proceedings of the Seventh Workshop on Statistical Machine Translation

2009

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The Crotal SRL System : a Generic Tool Based on Tree-structured CRF
Erwan Moreau | Isabelle Tellier
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL 2009): Shared Task

2008

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Robust Similarity Measures for Named Entities Matching
Erwan Moreau | François Yvon | Olivier Cappé
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)