This paper 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 ``co-requirement''. Second, it is also claimed that the set of words occurring in similar positions defines extensionally the requirements associated to these positions. The main aim of the learning strategy presented in this paper 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 paper. Extensive experimentation was performed on Portuguese text corpora.
