Building a Treebank for French

By Anne Abeillé, Lionel Clément, Alexandra Kinyon. International Conference on Language Resources and Evaluation, 2000

556 citations recorded by the source corpus.

We present a treebank project for French. We have annotated a newspaper corpus of 1 Million words with part of speech, inflection, compounds, lemmas and constituency. We describe the tagging and parsing phases of the project, and for each, the automatic tools, the guidelines and the validation process. We then present some uses of the corpus as well as some directions for future work.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/847f948aa549d4a09d2a3a25e00c90a989a8abd9

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In Artificial Intelligence

Citations in the corpus

FlauBERT: Unsupervised Language Model Pre-training for French (2019, methodology): “Our word representations are a concatenation of word embeddings and tag embeddings learned together with the model parameters on the French Treebank data itself, and at most one of (fastText, CamemBERT, FlauBERTBASE, FlauBERTBASE, mBERT) word vector.”
Introducing the SPMRL 2014 Shared Task on Parsing Morphologically-rich Languages (2014, methodology): “Regarding French, we used a new instance of the French Treebank (Abeillé et al., 2003) that includes multi-word expression (MWE) annotations, annotated at the morpho-syntactic level in both instances.”
From Balustrades to Pierre Vinken: Looking for Syntax in Transformer Self-Attentions (2019, methodology): “We use the factored lexicalized parsing models distributed together with the parser, which had been trained on standard constituency treebanks of the languages – English Penn Treebank (Marcus et al., 1993), German Negra Corpus (Skut et al., 1999), and French Treebank (Abeillé et al., 2003).”
Improved Transition-based Parsing by Modeling Characters instead of Words with LSTMs (2015, background): “…of the SPMRL shared task (Seddah et al., 2013; Seddah and Tsarfaty, 2014): Arabic (Maamouri et al., 2004), Basque (Aduriz et al., 2003), French (Abeill´e et al., 2003), German (Seeker and Kuhn, 2012), Hebrew (Sima’an et al., 2001), Hungarian (Vincze et al., 2010), Korean (Choi, 2013), Polish…”
CamemBERT: a Tasty French Language Model (2019, methodology): “Both improvements result in a 2.36 point increase in the F1 score with respect to the best SEM architecture (BiLSTM-CRF), giving CamemBERT the state of the art for NER on the FTB.”
Edinburgh Research Explorer Stanford University's Submissions to the WMT 2014 Translation Task (2014, methodology): “The French source data was tokenized similarly to the French Treebank (Abeillé et al., 2003) using the Stanford French tokenizer (Green et al., 2013b).”
Word Segmentation, Unknown-word Resolution, and Morphological Agreement in a Hebrew Parsing System (2013, methodology): “The PCFG-LA parsing methodology is very robust, producing state-of-the-art accuracies for English, as well as many other languages including German (Petrov and Klein 2008), French (Candito, Crabb´e, and Seddah 2009), and Chinese (Huang and Harper 2009).”
ModernBERT or DeBERTaV3? Examining Architecture and Data Influence on Transformer Encoder Models Performance (2025, unknown): “…including: • Question Answering (QA) : using FQuAD 1.0 d’Hoffschmidt et al. (2020) • Named Entity Recognition (NER) : on the 2008 FTB version (Abeill´e et al., 2000; Candito & Crabb´e, 2009) with NER annotations by Sagot et al. (Sagot et al., 2012) • Text Classification capabilities assessed…”