This article describes an unsupervised strategy to acquire syntactico-semantic requirements of nouns, verbs, and adjectives from partially parsed text corpora. The linguistic notion of requirement underlying this strategy is based on two specific assumptions. First, it is assumed that two words in a dependency are mutually required. This phenomenon is called here corequirement. Second, it is also claimed that the set of words occurring in similar positions defines extensionally the requirements associated with these positions. The main aim of the learning strategy presented in this article is to identify clusters of similar positions by identifying the words that define their requirements extensionally. This strategy allows us to learn the syntactic and semantic requirements of words in different positions. This information is used to solve attachment ambiguities. Results of this particular task are evaluated at the end of the article. Extensive experimentation was performed on Portuguese text corpora.

A statistics-based approach for clustering documents and for extracting cluster topics is described relevant (meaningful) expressions (REs) automatically extracted from corpora are used as clustering base features. These features are transformed and its number is strongly reduced in order to obtain a small set of document classification features. This is achieved on the basis of principal components analysis. Model-based clustering analysis finds the best number of clusters. Then, the most important REs are extracted from each cluster and taken as document cluster topics.

Some authors (Simard et al.; Melamed; Danielsson & Mühlenbock) have suggested measures of similarity of words in different languages so as to find extra clues for alignment of parallel texts. Cognate words, like ‘Parliament’ and ‘Parlement’, in English and French respectively, provide extra anchors that help to improve the quality of the alignment. In this paper, we will extend an alignment algorithm proposed by Ribeiro et al. using typical contiguous and non-contiguous sequences of characters extracted using a statistically sound method (Dias et al.). With these typical sequences, we are able to find more reliable correspondence points and improve the alignment quality without recurring to heuristics to identify cognates.

This paper describes a language independent method for alignment of parallel texts that makes use of homograph tokens for each pair of languages. In order to filter out tokens that may cause misalignment, we use confidence bands of linear regression lines instead of heuristics which are not theoretically supported. This method was originally inspired on work done by Pascale Fung and Kathleen McKeown, and Melamed, providing the statistical support those authors could not claim.

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