Francisco J. Ribadas

Phrase Similarity through the Edit Distance

This work intends to capture the concept of similarity between phrases. The algorithm is based on a dynamic programming approach integrating both the edit distance between parse trees and single-term similarity. Our work stresses the use of the underlying grammatical structure, which serves as a guide in the computation of semantic similarity between words. This proposal allows us to obtain a more accurate notion of semantic proximity at sentence level, without increasing the complexity of the pattern-matching algorithm on which it is based.

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.

COLE Experiments in the CLEF 2002 Spanish Monolingual Track

In this our first participation in CLEF, we applied Natural Language Processing techniques for single word and multiword term conflation. We tested several approaches at different levels of text processing in our experiments: first, we lemmatized the text to avoid inflectional variation; second, we expanded the queries through synonyms according to a fixed similarity threshold; third, we employed morphological families to deal with derivational variation; and fourth, we tested a mixed approach based on the employment of such families together with syntactic dependencies to deal with the syntactic content of the document.

Semantic similarity between sentences through approximate tree matching

We describe an algorithm to measure the similarity between sentences, integrating the edit distance between trees and single-term similarity techniques, and also allowing the pattern to be defined approximately, omitting some structural details. A technique of this kind is of interest in a variety of applications, such as information extraction/retrieval or question answering, where error-tolerant recognition allows incomplete sentences to be integrated in the computation process.

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.

Concept profiles for filtering parliamentary documents

Content-based recommender/filtering systems help to appropriately distribute information among the individuals or organizations that could consider it of interest. In this paper we describe a filtering system to deal with the problem of assigning documents to members of the parliament potentially interested on them. The proposed approach exploits subjects taken from a conceptual thesaurus to create the user profiles and to describe the documents to be filtered. The assignment of subjects to documents is modeled as a multilabel classification problem. Experiments with a real parliamentary corpus are reported, evaluating several methods to assign conceptual subjects to documents and to match those sets of subjects with user profiles.

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.

Cole experiments at CLEF 2003 in the Spanish monolingual track

In this our second participation in the CLEF Spanish monolingual track, we have continued applying Natural Language Processing techniques for single word and multi-word term conflation. Two different conflation approaches have been tested. The first approach is based on the lemmatization of the text in order to avoid inflectional variation. Our second approach consists of the employment of syntactic dependencies as complex index terms, in an attempt to solve the problems derived from syntactic variation and, in this way, to obtain more precise terms. Such dependencies are obtained through a shallow parser based on cascades of finite-state transducers.

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.