Strategies for Contiguous Multiword Expression Analysis and Dependency Parsing

International audienceIn this paper, we investigate various strategies to predict both syntactic dependency parsing and contiguous multiword expression (MWE) recognition, testing them on the dependency version of French Treebank \cite{abeille:04}, as instantiated in the SPMRL Shared Task \cite{spmrl:st:2013}. Our work focuses on using an alternative representation of syntactically regular MWEs, which captures their syntactic internal structure. We obtain a system with comparable performance to that of previous works on this dataset, but which predicts both syntactic dependencies and the internal structure of MWEs. This can be useful for capturing the various degrees of semantic compositionality of MWEs

Expériences d'analyse syntaxique statistique du français

National audienceWe show that we can acquire satisfactory parsing results for French from data induced from the French Treebank using an unlexicalised parsing algorithm, that learns a probabilistic contex-free grammar with latent annotations. We investigate various instantiations of the treebank, in order to improve the performance of the learnt parser.Nous montrons qu'il est possible d'obtenir une analyse syntaxique statistique satisfaisante pour le français sur du corpus journalistique, à partir des données issues du French Treebank du laboratoire LLF, à l'aide d'un algorithme d'analyse non lexicalisé

Improving generative statistical parsing with semi-supervised word clustering

short paper (4 pages)International audienceWe present a semi-supervised method to improve statistical parsing performance. We focus on the well-known problem of lexical data sparseness and present experiments of word clustering prior to parsing. We use a combination of lexicon-aided morphological clustering that preserves tagging ambiguity, and unsupervised word clustering, trained on a large unannotated corpus. We apply these clusterings to the French Treebank, and we train a parser with the PCFG-LA unlexicalized algorithm of Petrov et al. (2006). We find a gain in French parsing performance: from a baseline of F1=86.76% to F1=87.37% using morphological clustering, and up to F1=88.29% using further unsupervised clustering. This is the best known score for French probabilistic parsing. These preliminary results are encouraging for statistically parsing morphologically rich languages, and languages with small amount of annotated data

Le corpus Sequoia : annotation syntaxique et exploitation pour l'adaptation d'analyseur par pont lexical

National audienceWe present the building methodology and the properties of the Sequoia treebank, a freely available French corpus annotated following the French Treebank guidelines (Abeillé et Barrier, 2004). The Sequoia treebank comprises 3204 sentences (69246 tokens), from the French Europarl, the regional newspaper L'Est Républicain, the French Wikipedia and documents from the European Medicines Agency. We then provide a method for parser domain adaptation, that makes use of unsupervised word clusters. The method improves parsing performance on target domains (the domains of the Sequoia corpus), without degrading performance on source domain (the French treenbank test set), contrary to other domain adaptation techniques such as self-training.Nous présentons dans cet article la méthodologie de constitution et les caractéristiques du corpus Sequoia, un corpus en français, syntaxiquement annoté d'après un schéma d'annotation très proche de celui du French Treebank (Abeillé et Barrier, 2004), et librement disponible, en constituants et en dépendances. Le corpus comporte des phrases de quatre origines : Europarl français, le journal l'Est Républicain, Wikipédia Fr et des documents de l'Agence Européenne du Médicament, pour un total de 3204 phrases et 69246 tokens. En outre, nous présentons une application de ce corpus : l'évaluation d'une technique d'adaptation d'analyseurs syntaxiques probabilistes à des domaines et/ou genres autres que ceux du corpus sur lequel ces analyseurs sont entraînés. Cette technique utilise des clusters de mots obtenus d'abord par regroupement morphologique à l'aide d'un lexique, puis par regroupement non supervisé, et permet une nette amélioration de l'analyse des domaines cibles (le corpus Sequoia), tout en préservant le même niveau de performance sur le domaine source (le FTB), ce qui fournit un analyseur multi-domaines, à la différence d'autres techniques d'adaptation comme le self-training

Parsing word clusters

International audienceWe present and discuss experiments in statistical parsing of French, where terminal forms used during training and parsing are replaced by more general symbols, particularly clusters of words obtained through unsupervised linear clustering. We build on the work of Candito and Crabbé (2009) who proposed to use clusters built over slightly coarsened French inflected forms. We investigate the alternative method of building clusters over lemma/part-of-speech pairs, using a raw corpus automatically tagged and lemmatized. We find that both methods lead to comparable improvement over the baseline (we obtain F_1=86.20% and F_1=86.21% respectively, compared to a baseline of F_1=84.10%). Yet, when we replace gold lemma/POS pairs with their corresponding cluster, we obtain an upper bound (F_1=87.80) that suggests room for improvement for this technique, should tagging/lemmatisation performance increase for French. We also analyze the improvement in performance for both techniques with respect to word frequency. We find that replacing word forms with clusters improves attachment performance for words that are originally either unknown or low-frequency, since these words are replaced by cluster symbols that tend to have higher frequencies. Furthermore, clustering also helps significantly for medium to high frequency words, suggesting that training on word clusters leads to better probability estimates for these words

Introduction to the special issue on annotated corpora

International audienceLes corpus annotés sont toujours plus cruciaux, aussi bien pour la recherche scien- tifique en linguistique que le traitement automatique des langues. Ce numéro spécial passe brièvement en revue l’évolution du domaine et souligne les défis à relever en restant dans le cadre actuel d’annotations utilisant des catégories analytiques, ainsi que ceux remettant en question le cadre lui-même. Il présente trois articles, l’un concernant l’évaluation de la qualité d’annotation, et deux concernant des corpus arborés du français, l’un traitant du plus ancien projet de corpus arboré du français, le French Treebank, le second concernant la conversion de corpus français dans le schéma interlingue des Universal Dependencies, offrant ainsi une illustration de l’histoire du développement des corpus arborés.Annotated corpora are increasingly important for linguistic scholarship, science and technology. This special issue briefly surveys the development of the field and points to challenges within the current framework of annotation using analytical categories as well as challenges to the framework itself. It presents three articles, one concerning the evaluation of the quality of annotation, and two concerning French treebanks, one dealing with the oldest project for French, the French Treebank, the second concerning the conversion of French corpora into the cross-lingual framework of Universal Dependencies, thus offering an illustration of the history of treebank development worldwide

Semi-Automatic Deep Syntactic Annotations of the French Treebank

International audienceWe describe and evaluate the semi-automatic addition of a deep syntactic layer to the French Treebank (Abeillé and Barrier [1]), using an existing scheme (Candito et al. [6]). While some rare or highly ambiguous deep phenomena are handled manually, the remainings are derived using a graph-rewriting system (Ribeyre et al. [22]). Although not manually corrected, we think the resulting Deep Representations can pave the way for the emergence of deep syntactic parsers for French

Lemmatization and lexicalized statistical parsing of morphologically rich languages: the case of French

This paper shows that training a lexicalized parser on a lemmatized morphologically-rich treebank such as the French Treebank slightly improves parsing results. We also show that lemmatizing a similar in size subset of the English Penn Treebank has almost no effect on parsing performance with gold lemmas and leads to a small drop of performance when automatically assigned lemmas and POS tags are used. This highlights two facts: (i) lemmatization helps to reduce lexicon data-sparseness issues for French, (ii) it also makes the parsing process sensitive to correct assignment of POS tags to unknown words

Corpus annotation within the French FrameNet: a domain-by-domain methodology

International audienceThis paper reports on the development of a French FrameNet, within the ASFALDA project. While the first phase of the project focused on the development of a French set of frames and corresponding lexicon (Candito et al., 2014), this paper concentrates on the subsequent corpus annotation phase, which focused on four notional domains (commercial transactions, cognitive stances, causality and verbal communication). Given full coverage is not reachable for a relatively " new " FrameNet project, we advocate that focusing on specific notional domains allowed us to obtain full lexical coverage for the frames of these domains, while partially reflecting word sense ambiguities. Furthermore, as frames and roles were annotated on two French Treebanks (the French Treebank (Abeillé and Barrier, 2004) and the Sequoia Treebank (Candito and Seddah, 2012), we were able to extract a syntactico-semantic lexicon from the annotated frames. In the resource's current status, there are 98 frames, 662 frame-evoking words, 872 senses, and about 13000 annotated frames, with their semantic roles assigned to portions of text. The French FrameNet is freely available at alpage.inria.fr/asfalda

Analyse syntaxique du français : des constituants aux dépendances

10 pagesInternational audienceThis paper describes a technique for both constituent and dependency parsing. Parsing proceeds by adding functional labels to the output of a constituent parser trained on the French Treebank in order to further extract typed dependencies. On the one hand we specify on formal and linguistic grounds the nature of the dependencies to output as well as the conversion algorithm from the French Treebank to this dependency representation. On the other hand, we describe a class of algorithms that allows to perform the automatic labeling of the functions from the output of a constituent based parser. We specifically focus on discriminative learning methods for functional labelling