Search Results for author: Phillippe Langlais

Found 16 papers, 1 papers with code

HardEval: Focusing on Challenging Tokens to Assess Robustness of NER

no code implementations LREC 2020 Gabriel Bernier-Colborne, Phillippe Langlais

To assess the robustness of NER systems, we propose an evaluation method that focuses on subsets of tokens that represent specific sources of errors: unknown words and label shift or ambiguity.


SEDAR: a Large Scale French-English Financial Domain Parallel Corpus

1 code implementation LREC 2020 Abbas Ghaddar, Phillippe Langlais

This paper describes the acquisition, preprocessing and characteristics of SEDAR, a large scale English-French parallel corpus for the financial domain.

Domain Adaptation Machine Translation

Contextualized Word Representations from Distant Supervision with and for NER

no code implementations WS 2019 Abbas Ghaddar, Phillippe Langlais

We describe a special type of deep contextualized word representation that is learned from distant supervision annotations and dedicated to named entity recognition.

Named Entity Recognition NER

Users and Data: The Two Neglected Children of Bilingual Natural Language Processing Research

no code implementations WS 2017 Phillippe Langlais

Despite numerous studies devoted to mining parallel material from bilingual data, we have yet to see the resulting technologies wholeheartedly adopted by professional translators and terminologists alike.

Machine Translation

WikiCoref: An English Coreference-annotated Corpus of Wikipedia Articles

no code implementations LREC 2016 Abbas Ghaddar, Phillippe Langlais

This paper presents WikiCoref, an English corpus annotated for anaphoric relations, where all documents are from the English version of Wikipedia.

Coreference Resolution

Hashtag Occurrences, Layout and Translation: A Corpus-driven Analysis of Tweets Published by the Canadian Government

no code implementations LREC 2014 Fabrizio Gotti, Phillippe Langlais, Atefeh Farzindar

A manual analysis of the bilingual alignment of 5000 hashtags shows that 5{\%} (French) to 18{\%} (English) of them don{'}t have a counterpart in their containing tweet{'}s translation.

Information Retrieval Machine Translation +1

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