Un entorno para la extracción de información semántica del diccionario VOX

Postprint (published version

A Class-based approach to learn appropriate selectional restrictions from a parsed corpus

We present a methodology to extract Selectional Restrictions at a variable level of abstraction from phrasally analyzed corpora. The method relays in the use of a wide-coverage noun taxonomy and a statistical measure of the co-occurrence of linguistic items. Some experimental results about the performance of the method are provided.Postprint (published version

Learning more appropriate selectional restrictions

In this paper we present further work on learning SRs from on-line corpora. The technique relays in the use of a wide-coverage noun taxonomy and a statistical measure of co-occurrence to generalize from words to semantic classes. We analyze some experimental results, detect some unsolved problems and outline possible lines of research. We claim for the need of objective evaluation measures for the SRs learning task; presenting and discussing some of them. Some variations on the basic technique, affecting the statistical association measure and thresholding, are presented and discussed. Some experimental results on these variations are reported. Some of these variations seem to improve the performance. Concluding, we summarize the future lines of research we think can lead to further improvements.Postprint (published version

A Class-based approach to learn appropriate selectional restrictions from a parsed corpus

We present a methodology to extract Selectional Restrictions at a variable level of abstraction from phrasally analyzed corpora. The method relays in the use of a wide-coverage noun taxonomy and a statistical measure of the co-occurrence of linguistic items. Some experimental results about the performance of the method are provided

Learning more appropriate selectional restrictions

In this paper we present further work on learning SRs from on-line corpora. The technique relays in the use of a wide-coverage noun taxonomy and a statistical measure of co-occurrence to generalize from words to semantic classes. We analyze some experimental results, detect some unsolved problems and outline possible lines of research. We claim for the need of objective evaluation measures for the SRs learning task; presenting and discussing some of them. Some variations on the basic technique, affecting the statistical association measure and thresholding, are presented and discussed. Some experimental results on these variations are reported. Some of these variations seem to improve the performance. Concluding, we summarize the future lines of research we think can lead to further improvements

A Class-based approach to learn appropriate selectional restrictions from a parsed corpus

We present a methodology to extract Selectional Restrictions at a variable level of abstraction from phrasally analyzed corpora. The method relays in the use of a wide-coverage noun taxonomy and a statistical measure of the co-occurrence of linguistic items. Some experimental results about the performance of the method are provided

Design and implementation of a reasoning engine using generalized meta-rules to express control knowledge

This paper describes the design and implementation of the reasoning engine developed for the interpretation of FLORIAN rule language. A key feature of the language is to allow the specification of control knowledge using generalized meta-rules. The user can define how to solve conflicts at the object-level, at the meta-level or at any higher level using meta-i-rules. Object-level rules and generalized meta-i-rules share the same rule format. Several examples of meta-rules and higher level rules are presented using the rule syntax. The architecture and working of the rule interpreter is analysed describing the main algorithms and abstract data types implementing the reasoning engine.Postprint (published version

Design and implementation of a reasoning engine using generalized meta-rules to express control knowledge

This paper describes the design and implementation of the reasoning engine developed for the interpretation of FLORIAN rule language. A key feature of the language is to allow the specification of control knowledge using generalized meta-rules. The user can define how to solve conflicts at the object-level, at the meta-level or at any higher level using meta-i-rules. Object-level rules and generalized meta-i-rules share the same rule format. Several examples of meta-rules and higher level rules are presented using the rule syntax. The architecture and working of the rule interpreter is analysed describing the main algorithms and abstract data types implementing the reasoning engine

Translation equivalence via lexicon: a study on tlinks

This paper provides a study and suggests an extension of the translation links mechanism. It explains the process of the tlink generation experiment within the AcquilexII framework and it extensively discusses the results by giving a lot of illustrative examples.Postprint (published version