Una propuesta para mejorar la completitud de requisitos utilizando un enfoque lingüístico

La calidad de los productos de software está estrechamente relacionada con la calidad de los requisitos especificados desde las primeras etapas del proceso de desarrollo; las propuestas encaminadas a la especificación de requisitos realizan incipientes esfuerzos para lograr que los requisitos del software sean lo suficientemente completos como para lograr la traducción de las necesidades y expectativas de los usuarios al producto final. En este artículo se presenta una propuesta para mejorar la calidad, en cuanto a completitud, de especificaciones de requisitos escritas en un subconjunto del español denominado español restringido, utilizando para ello un enfoque lingüístico basado en la gramática de casos./Software product quality is closely linked with requirements quality from development process initial stages; proposals directed to requirements specification make incipient efforts to reach software requirements complete enough to reach the translation of user needs and expectations into the final product. In this paper we present a proposal for quality enhancement, especially in completeness, of requirements specifications written in restricted Spanish, a subset from Spanish, using a linguistic Case-Grammar- based approach to reach this goal

Mapping Lexical Entries in a Verbs Database to WordNet Senses

This paper describes automatic techniques for mapping 9611 entries in a database of English verbs to WordNet senses. The verbs were initially grouped into 491 classes based on syntactic features. Mapping these verbs into WordNet senses provides a resource that supports disambiguation in multilingual applications such as machine translation and cross-language information retrieval. Our techniques make use of (1) a training set of 1791 disambiguated entries, representing 1442 verb entries from 167 classes; (2) word sense probabilities, from frequency counts in a tagged corpus; (3) semantic similarity of WordNet senses for verbs within the same class; (4) probabilistic correlations between WordNet data and attributes of the verb classes. The best results achieved 72% precision and 58% recall, versus a lower bound of 62% precision and 38% recall for assigning the most frequently occurring WordNet sense, and an upper bound of 87% precision and 75% recall for human judgment