Vicente Carrillo

Mixed Parsing of Tree Insertion and Tree Adjoining Grammars

Adjunction is a powerful operation that makes Tree Adjoining Grammar (TAG) useful for describing the syntactic structure of natural languages. In practice, a large part of wide coverage grammars written following the TAG formalism is formed by trees that can be combined by means of the simpler kind of adjunction defined for Tree Insertion Grammar. In this paper, we describe a parsing algorithm that makes use of this characteristic to reduce the practical complexity of TAG parsing: the expensive standard adjunction operation is only considered in those cases in which the simpler cubic-time adjunction cannot be applied.

A Review of Early-Based Parser for TIG

Tree Insertion Grammar (TIG) is a compromise between Context-Free Grammars (CFG) and Tree Adjoining Grammars (TAG), that combines the efficiency of the former with the strong lexicalizing power of the latter. In this paper, we present a plain representation of TIG elementary trees that can be used directly as the input grammar for the original Earley parser without the additional considerations established in the Schabes and Waters Earley-based parser for TIG.

Named Entity Recognition Through Corpus Transformation and System Combination

In this paper we investigate the way of combining different taggers to improve their performance in the named entity recognition task. The main resources used in our experiments are the publicly available taggers TnT and TBL and a corpus of Spanish texts in which named entities occurrences are tagged with BIO tags. We have defined three transformations that provide us three additional versions of the training corpus. The transformations change either the words or the tags, and the three of them improve the results of TnT and TBL when they are trained with the original version of the corpus. With the four versions of the corpus and the two taggers, we have eight different models that can be combined with several techniques. The experiments carried out show that using machine learning techniques to combine them the performance improves considerably. We improve the baselines for TnT (Fβ= 1 value of 85.25) and TBL (Fβ= 1 value of 87.45) up to a value of 90.90 in the best of our experiments.

Left Corner Parser for Tree Insertion Grammars

Tree Adjoining Grammar (TAG) is a grammar formalism that has become very popular for the description of natural languages, however, this context-sensitive formalism entails important computation costs (O(n6)-time). Tree Insertion Grammar (TIG) is a compromise between Context Free Grammar (CFG) and TAG that can be parsed in O(n3) - time. In the literature, just two Earley-like parsers for TIGs have been defined. In this paper, we define a new variant of Earley-like parser for TIGs. In order to improve the performance of this parser, we show how the left corner relation for CFG can be generalized to the case of TIG and we present an efficient parser for TIG that uses this relation.Tree Adjoining Grammar (TAG) is a grammar formalism that has become very popular for the description of natural languages, however, this context-sensitive formalism entails important computation costs (O(n6)-time). Tree Insertion Grammar (TIG) is a compromise between Context Free Grammar (CFG) and TAG that can be parsed in O(n3) - time. In the literature, just two Earley-like parsers for TIGs have been defined. In this paper, we define a new variant of Earley-like parser for TIGs. In order to improve the performance of this parser,w e show how the left corner relation for CFG can be generalized to the case of TIG and we present an efficient parser for TIG that uses this relation.