Bare-Bones Dependency Parsing — A Case for Occam's Razor?

Proceedings of the 18th Nordic Conference of Computational Linguistics NODALIDA 2011. Editors: Bolette Sandford Pedersen, Gunta Nešpore and Inguna Skadiņa. NEALT Proceedings Series, Vol. 11 (2011), 6-11. © 2011 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/16955

Learning Semantic Correspondences in Technical Documentation

We consider the problem of translating high-level textual descriptions to formal representations in technical documentation as part of an effort to model the meaning of such documentation. We focus specifically on the problem of learning translational correspondences between text descriptions and grounded representations in the target documentation, such as formal representation of functions or code templates. Our approach exploits the parallel nature of such documentation, or the tight coupling between high-level text and the low-level representations we aim to learn. Data is collected by mining technical documents for such parallel text-representation pairs, which we use to train a simple semantic parsing model. We report new baseline results on sixteen novel datasets, including the standard library documentation for nine popular programming languages across seven natural languages, and a small collection of Unix utility manuals.Comment: accepted to ACL-201

Adding frequencies to the LGLex lexicon with IRASUBCAT

We present a method for enlarge a lexicon (with frequencies information), that is useful for parsing and others NLP applications. We show an example enlarging the verbal LGLex lexicon of French [8], using several corpora extracted from the evaluation campaign for French parsers Passage [5]. To do that, we use the results of the frmg parser [7] with IRASubcat, a tool that automatically acquires subcategorization frames from corpus in any language and that also allows to complete an existing lexicon. We obtain the frequencies of occurrence for each input and each subcategorization frame for 14,068 distinct lemmas.Sociedad Argentina de Informática e Investigación Operativ

Improving a symbolic parser through partially supervised learning

International audienceRecently, several statistical parsers have been trained and evaluated on the dependency version of the French TreeBank (FTB). However, older symbolic parsers still exist, including FRMG, a wide coverage TAG parser. It is interesting to compare these different parsers, based on very different approaches, and explore the possibilities of hybridization. In particular, we explore the use of partially supervised learning techniques to improve the performances of FRMG to the levels reached by the statistical parsers.Récemment, plusieurs analyseurs syntaxiques statistiques ont été entrainés et évalués sur la version en dépendances du French TreeBank (FTB). Cependant, des analyseurs symboliques plus anciens continuent à exister, dont FRMG, un analyseur TAG à large couverture. Il est intéressant de comparer ces divers analyseurs, fondés sur des approches très différentes et d'explorer des possibilités d'hybridation. En particulier, nous explorons l'utilisation de techniques d'apprentissage partiellement supervisé pour améliorer les performances de FRMG au niveau de celles des analyseurs statistiques

Effectively long-distance dependencies in French : annotation and parsing evaluation

International audienceWe describe the annotation of cases of extraction in French, whose previous annotations in the available French treebanks were insufficient to recover the correct predicate-argument dependency between the extracted element and its head. These cases are special cases of LDDs, that we call effectively long- distance dependencies (eLDDs), in which the extracted element is indeed separated from its head by one or more intervening heads (instead of zero, one or more for the general case). We found that extraction of a dependent of a finite verb is very rarely an eLDD (one case out of 420 000 tokens), but eLDDs corresponding to extraction out of infinitival phrase is more fre- quent (one third of all occurrences of accusative relative pronoun que), and eLDDs with extraction out of NPs are quite common (2/3 of the occurrences of relative pronoun dont). We also use the annotated data in statistical depen- dency parsing experiments, and compare several parsing architectures able to recover non-local governors for extracted elements

Transition-based dependency parsing as latent-variable constituent parsing

We provide a theoretical argument that a common form of projective transition-based dependency parsing is less powerful than constituent parsing using latent variables. The argument is a proof that, under reasonable assumptions, a transition-based dependency parser can be converted to a latent-variable context-free grammar producing equivalent structures.Postprin

Treebank-Based Deep Grammar Acquisition for French Probabilistic Parsing Resources

Motivated by the expense in time and other resources to produce hand-crafted grammars, there has been increased interest in wide-coverage grammars automatically obtained from treebanks. In particular, recent years have seen a move towards acquiring deep (LFG, HPSG and CCG) resources that can represent information absent from simple CFG-type structured treebanks and which are considered to produce more language-neutral linguistic representations, such as syntactic dependency trees. As is often the case in early pioneering work in natural language processing, English has been the focus of attention in the first efforts towards acquiring treebank-based deep-grammar resources, followed by treatments of, for example, German, Japanese, Chinese and Spanish. However, to date no comparable large-scale automatically acquired deep-grammar resources have been obtained for French. The goal of the research presented in this thesis is to develop, implement, and evaluate treebank-based deep-grammar acquisition techniques for French. Along the way towards achieving this goal, this thesis presents the derivation of a new treebank for French from the Paris 7 Treebank, the Modified French Treebank, a cleaner, more coherent treebank with several transformed structures and new linguistic analyses. Statistical parsers trained on this data outperform those trained on the original Paris 7 Treebank, which has five times the amount of data. The Modified French Treebank is the data source used for the development of treebank-based automatic deep-grammar acquisition for LFG parsing resources for French, based on an f-structure annotation algorithm for this treebank. LFG CFG-based parsing architectures are then extended and tested, achieving a competitive best f-score of 86.73% for all features. The CFG-based parsing architectures are then complemented with an alternative dependency-based statistical parsing approach, obviating the CFG-based parsing step, and instead directly parsing strings into f-structures

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

Synthetic Treebanking for Cross-Lingual Dependency Parsing

accepted to appear in the special issue on Cross-Language Algorithms and ApplicationsPeer reviewe