This paper presents a number of linguistic and computational issues identified during the implementation of a general use grammar checker for contemporary Brazilian Portuguese, ReGra, that has been incorporated in the word processor REDATOR by Itautec/Philco (Brazil). Two main strategies were employed in the implementation of correction rules: an error-driven, localist approach based on the identification of patterns indicative of grammatical mistakes; and a more generic approach that requires automatic syntactic analysis. In this discussion, particular emphasis is given to the development of a parser based on a phrase structure grammar comprising over 600 production rules. As for the computational performance, ReGra permits texts to be revised at a rate of ca. 200 words per second.

Currently, word tokenization and segmentation are still a hot topic in natural language processing, especially for languages like Chinese in which there is no blank space for word delimitation. Three major problems are faced: (1) tokenizing direction and efficiency; (2) insufficient tokenization dictionary and new words; and (3) ambiguity of tokenization and segmentation. Most existing tokenization and segmentation methods have not dealt with the above problems together. To tackle the three problems in one basket, this paper presents a novel dictionary-based method called the Splitting-Merging Model (SMM) for Chinese word tokenization and segmentation. It uses the mutual information of Chinese characters to find the boundaries and the non-boundaries of Chinese words, and finally leads to a word segmentation by resolving ambiguities and detecting new words.

Semantically tagging a corpus is useful for many intermediate NLP tasks such as: acquisition of word argument structures in sublanguages; acquisition of syntactic disambiguation cues; terminology learning; etc. The general idea is that semantic tags allow the generalization of observed word patterns, and facilitate the discovery of recurrent sublanguage phenomena and selectional rules of various types. Yet, as opposed to POS tags in morphology, there is no consensus in the literature about the type and granularity of the semantic tags to be used. In this paper, we argue that an appropriate selection of semantic tags should be domain-dependent. We propose a method by which we select from WordNet an inventory of semantic tags that are ‘optimal’ for a given corpus, according to a scoring function defined as a linear combination of general and corpus-dependent performance factors. We believe that an optimal selection of a category inventory is a necessary premise for obtaining better results in all lexically learning algorithms that are based on, or concerned with, semantic categorization of words. Furthermore, an adequate inventory (one which intuitively ‘fits’ with the semantics of a domain, e.g. phenomenon for Natural Science, or part, piece for a technical handbook) may facilitate the manual annotation of large corpora.

Quantified expressions present an interesting case for understanding pronominal reference because the quantifier which appears in the expression affects how the pronoun will be understood. The quantifier determines which sets will be placed in focus and predicts the relation between the sentence containing the quantifier and the following sentence (Moxey and Sanford 1993a). This information is essential to understanding the pronominal reference. In this paper, we discuss how a natural language processing system can take advantage of this information to understand pronominal references to quantified expressions.