446 research outputs found
A Transition-Based Directed Acyclic Graph Parser for UCCA
We present the first parser for UCCA, a cross-linguistically applicable
framework for semantic representation, which builds on extensive typological
work and supports rapid annotation. UCCA poses a challenge for existing parsing
techniques, as it exhibits reentrancy (resulting in DAG structures),
discontinuous structures and non-terminal nodes corresponding to complex
semantic units. To our knowledge, the conjunction of these formal properties is
not supported by any existing parser. Our transition-based parser, which uses a
novel transition set and features based on bidirectional LSTMs, has value not
just for UCCA parsing: its ability to handle more general graph structures can
inform the development of parsers for other semantic DAG structures, and in
languages that frequently use discontinuous structures.Comment: 16 pages; Accepted as long paper at ACL201
Une approche par boosting à la sélection de modèles pour l’analyse syntaxique statistique
International audienceIn this work we present our approach to model selection for statistical parsing via boosting. The method is used to target the inefficiency of current feature selection methods, in that it allows a constant feature selection time at each iteration rather than the increasing selection time of current standard forward wrapper methods. With the aim of performing feature selection on very high dimensional data, in particular for parsing morphologically rich languages, we test the approach, which uses the multiclass AdaBoost algorithm SAMME (Zhu et al., 2006), on French data from the French Treebank, using a multilingual discriminative constituency parser (Crabbé, 2014). Current results show that the method is indeed far more efficient than a naïve method, and the performance of the models produced is promising, with F-scores comparable to carefully selected manual models. We provide some perspectives to improve on these performances in future work
Une approche par boosting à la sélection de modèles pour l’analyse syntaxique statistique
International audienceIn this work we present our approach to model selection for statistical parsing via boosting. The method is used to target the inefficiency of current feature selection methods, in that it allows a constant feature selection time at each iteration rather than the increasing selection time of current standard forward wrapper methods. With the aim of performing feature selection on very high dimensional data, in particular for parsing morphologically rich languages, we test the approach, which uses the multiclass AdaBoost algorithm SAMME (Zhu et al., 2006), on French data from the French Treebank, using a multilingual discriminative constituency parser (Crabbé, 2014). Current results show that the method is indeed far more efficient than a naïve method, and the performance of the models produced is promising, with F-scores comparable to carefully selected manual models. We provide some perspectives to improve on these performances in future work
Because Syntax does Matter: Improving Predicate-Argument Structures Parsing Using Syntactic Features
International audienceParsing full-fledged predicate-argument structures in a deep syntax framework requires graphs to be predicted. Using the DeepBank (Flickinger et al., 2012) and the Predicate-Argument Structure treebank (Miyao and Tsujii, 2005) as a test field, we show how transition-based parsers, extended to handle connected graphs, benefit from the use of topologically different syntactic features such as dependencies, tree fragments, spines or syntactic paths, bringing a much needed context to the parsing models, improving notably over long distance dependencies and elided coordinate structures. By confirming this positive impact on an accurate 2nd-order graph-based parser (Martins and Almeida, 2014), we establish a new state-of-the-art on these data sets
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