Victor M. Darriba

On the Usefulness of Extracting Syntactic Dependencies for Text Indexing

In recent years, there has been a considerable amount of interest in using Natural Language Processing in Information Retrieval research, with specific implementations varying from the word-level morphological analysis to syntactic parsing to conceptual-level semantic analysis. In particular, different degrees of phrase-level syntactic information have been incorporated in information retrieval systems working on English or Germanic languages such as Dutch. In this paper we study the impact of using such information, in the form of syntactic dependency pairs, in the performance of a text retrieval system for a Romance language, Spanish.

Regional Least-Cost Error Repair

We describe an algorithm to deal with automatic error repair over unrestricted context-free languages. The method relies on a regional least-cost repair strategy with validation, gathering all relevant information in the context of the error location. The system guarantees the asymptotic equivalence with global repair strategies.

A formal frame for robust parsing

A robust parser for context-free grammars, based on a dynamic programming architecture, is described. We integrate a regional error repair algorithm and a strategy to deal with incomplete sentences including unknown parts of unknown length. Experimental tests prove the validity of the approach, illustrating the perspectives for its application in real systems over a variety of different situations, as well as the causes underlying the computational behavior observed.

Approximate Pattern Matching in Shared-Forest

We present a proposal intended to demonstrate the applicability of tabulation techniques to pattern recognition problems, when dealing with structures sharing some common parts. This work in motivated by the study of information retrieval for textual databases, using pattern matching as a basis for querying data.

Parsing Incomplete Sentences Revisited

We describe a context-free parsing algorithm to deal with incomplete sentences, including unknown parts of unknown length. It produces a finite shared-forest compiling all parses, often infinite in number, that could account for both the error and the missing parts.

Robust parsing using dynamic programming

A robust parser for context-free grammars, based on a dynamic programming architecture, is described. We integrate a regional error repair algorithm and a strategy to deal with incomplete sentences including unknown parts of unknown length. Experimental tests prove the validity of the approach, illustrating the perspectives for its application in real systems over a variety of different situations, as well as the causes underlying the computational behavior observed.

Approximate VLDC Pattern Matching in Shared-Forest

We present a matching-based proposal intended to deal with querying for structured text databases. Our approach extends approximate VLDC matching techniques by allowing a query to exploit sharing of common parts between patterns used to index the document.

Multiple label text categorization on a hierarchical thesaurus

In this paper we describe our work on the automatic association of relevant topics, taken from a structured thesaurus, to documents written in natural languages. The approach we have followed models thesaurus topic assignment as a multiple label classification problem, where the whole set of possible classes is hierarchically organized.

Generation of incremental parsers

An incremental development environment for unrestricted context-free languages is described and tested. Our proposal includes a parse generator, an incremental facility to make the overall parsing efficient in the context of program development; and a graphical interface that provides a complete set of customization and trace facilities. The tool, baptized ICE after Incremental Context-Free Environment, appears to be superior to other general context-free parsing environments and is comparable to deterministic ones, when the context is not ambiguous.