Enforcing Subcategorization Constraints in a Parser Using Sub-parses Recombining

International audienceTreebanks are not large enough to adequately model subcategorization frames of predicative lexemes, which is an important source of lexico-syntactic constraints for parsing. As a consequence, parsers trained on such treebanks usually make mistakes when selecting the arguments of predicative lexemes. In this paper, we propose an original way to correct subcategorization errors by combining sub-parses of a sentence S that appear in the list of the n-best parses of S. The subcategorization information comes from three different resources, the first one is extracted from a treebank, the second one is computed on a large corpora and the third one is an existing syntactic lexicon. Experiments on the French Treebank showed a 15.24% reduction of erroneous subcategorization frames (SF) selections for verbs as well as a relative decrease of the error rate of 4% Labeled Accuracy Score on the state of the art parser on this treebank

Enforcing Subcategorization Constraints in a Parser Using Sub-parses Recombining

International audienceTreebanks are not large enough to adequately model subcategorization frames of predicative lexemes, which is an important source of lexico-syntactic constraints for parsing. As a consequence, parsers trained on such treebanks usually make mistakes when selecting the arguments of predicative lexemes. In this paper, we propose an original way to correct subcategorization errors by combining sub-parses of a sentence S that appear in the list of the n-best parses of S. The subcategorization information comes from three different resources, the first one is extracted from a treebank, the second one is computed on a large corpora and the third one is an existing syntactic lexicon. Experiments on the French Treebank showed a 15.24% reduction of erroneous subcategorization frames (SF) selections for verbs as well as a relative decrease of the error rate of 4% Labeled Accuracy Score on the state of the art parser on this treebank

Sous-catégorisation en pour et syntaxe lexicale

National audienceSub-categorizedargumentsintroducedbytheFrenchprepositionpourhasbeenunder-studiedinprevious work, as can be seen from the incompleteness of existing lexical-syntactic resources in that regard. In this paper, we briefly introduce the various types of sub-categorization in pour, which are to be distinguished from occurrences of pour as a discourse connective. We describe how we added arguments in pour within the syntactic lexicon Lefff, thus refining sub-categorization information for many verbal, nominal, adjectival and adverbial entries.La sous-catégorisation d'arguments introduits par la préposition pour a été sous-étudiée par le passé, comme en témoigne l'incomplétude des ressources lexico-syntaxiques existantes sur ce point. Dans cet article, nous présentons rapidement les différents types de sous-catégorisation en pour, qui contrastent avec les emplois de pour comme connecteur de discours. Nous décrivons l'intégration des arguments en pour au lexique syntaxique Lefff, enrichissant ainsi les informations de sous-catégorisation de nombreuses entrées verbales, nominales, adjectivales et adverbiales

Sous-catégorisation en pour et syntaxe lexicale

National audienceSub-categorizedargumentsintroducedbytheFrenchprepositionpourhasbeenunder-studiedinprevious work, as can be seen from the incompleteness of existing lexical-syntactic resources in that regard. In this paper, we briefly introduce the various types of sub-categorization in pour, which are to be distinguished from occurrences of pour as a discourse connective. We describe how we added arguments in pour within the syntactic lexicon Lefff, thus refining sub-categorization information for many verbal, nominal, adjectival and adverbial entries.La sous-catégorisation d'arguments introduits par la préposition pour a été sous-étudiée par le passé, comme en témoigne l'incomplétude des ressources lexico-syntaxiques existantes sur ce point. Dans cet article, nous présentons rapidement les différents types de sous-catégorisation en pour, qui contrastent avec les emplois de pour comme connecteur de discours. Nous décrivons l'intégration des arguments en pour au lexique syntaxique Lefff, enrichissant ainsi les informations de sous-catégorisation de nombreuses entrées verbales, nominales, adjectivales et adverbiales

Unsupervised extraction of semantic relations using discourse cues

International audienceThis paper presents a knowledge base containing triples involving pairs of verbs associated with semantic or discourse relations. The relations in these triples are marked by discourse connectors between two adjacent instances of the verbs in the triple in the large French corpus, frWaC. We detail several measures that evaluate the relevance of the triples and the strength of their association. We use manual annotations to evaluate our method, and also study the coverage of our resource with respect to the discourse annotated corpus Annodis. Our positive results show the potential impact of our resource for discourse analysis tasks as well as other semantically oriented tasks like temporal and causal information extractio

A Supervised Approach for Enriching the Relational Structure of Frame Semantics in FrameNet

Frame semantics is a theory of linguistic meanings, and is considered to be a useful framework for shallow semantic analysis of natural language. FrameNet, which is based on frame semantics, is a popular lexical semantic resource. In addition to providing a set of core semantic frames and their frame elements, FrameNet also provides relations between those frames (hence providing a network of frames i.e. FrameNet). We address here the limited coverage of the network of conceptual relations between frames in FrameNet, which has previously been pointed out by others. We present a supervised model using rich features from three different sources: structural features from the existing FrameNet network, information from the WordNet relations between synsets projected into semantic frames, and corpus-collected lexical associations. We show large improvements over baselines consisting of each of the three groups of features in isolation. We then use this model to select frame pairs as candidate relations, and perform evaluation on a sample with good precision