Jean Véronis

Text Encoding Initiative

This paper traces the history of the Text Encoding Initiative, through the Vassar Conference and the Poughkeepsie Principles to the publication, in May 1994, of the Guidelines for the Electronic Text Encoding and Interchange. The authors explain the types of questions that were raised, the attempts made to resolve them, the TEl projects aims, the general organization of the TEl committees, and they discuss the projects future.

Knowledge Extraction from Machine-Readable Dictionaries: An Evaluation

Machine-readable versions of everyday dictionaries have been seen as a likely source of information for use in natural language processing because they contain an enormous amount of lexical and semantic knowledge. However, after 15 years of research, the results appear to be disappointing. No comprehensive evaluation of machine-readable dictionaries (MRDs) as a knowledge source has been made to date, although this is necessary to determine what, if anything, can be gained from MRD research. To this end, this paper will first consider the postulates upon which MRD research has been based over the past fifteen years, discuss the validity of these postulates, and evaluate the results of this work. We will then propose possible future directions and applications that may exploit these years of effort, in the light of current directions in not only NLP research, but also fields such as lexicography and electronic publishing.

Outline of a model for lexical databases

In this paper we show that previously applied data models are inadequate for lexical databases. In particular, we show that relational data models, including unnormalized models which allow the nesting of relations, cannot fully capture the structural properties of lexical information. We propose an alternative feature-based model which allows for a full representation of sense nesting and defines a factoring mechanism that enables the elimination of redundant information. We then demonstrate that feature structures map readily to an object-oriented data model and show how our model can be implemented in an object-oriented DBMS.

An assessment of semantic information automatically extracted from machine readable dictionaries

In this paper we provide a quantitative evaluation of information automatically extracted from machine readable dictionaries. Our results show that for any one dictionary, 55--70% of the extracted information is garbled in some way. However, we show that these results can be dramatically reduced to about 6% by combining the information extracted from five dictionaries. It therefore appears that even if individual dictionaries are an unreliable source of semantic information, multiple dictionaries can play an important role in building large lexical-semantic databases.

Artificial intelligence and the study of literary narrative

Abstract This paper tries to show that some cross-fertilization between the fields of Artificial Intelligence (AI) and literary studies could be beneficial to both fields. Consideration of what is required to understand literary narrative can provide both a new set of challenging AI problems and greater insight into the mechanisms that contribute to the meaning of narrative texts in general. Literary studies can also benefit by re-casting their assumptions and theories in terms formal enough to lead to experiments on computers. We show that current AI models of story understanding require some fundamental changes in order to handle literary narrative, in areas such as the representation of sentence meaning, the understanding of a texts purpose and themes, the derivation of meaning from non-narrative textual features, and the need for more explicit and flexible models of the readers activity, goals, and abilities. We propose a general model of understanding that requires an interpreter capable of simulating different readers and operating at a level outside and surrounding those of current AI models.