Contrast Relation Recognition in Persian discourse using supervised learning methods

Discourse is any section or part of the language used to establish communications. Discourse Relations Recognition System recognizes the relations between textual units of a discourse. Contrast is one of the relations in Persian discourse. Recognition of this relation helps produce and understand the discourse. It can be used in variant systems such as summarization systems, interpretation systems, and so on. The contrast relation can be recognized using certain contrast relation markers such as “اما” and “ولی” Nevertheless, these markers are sometimes omitted, making trouble for relation recognition. In these cases, features such as tense of verbs, word pairs, etc. should be used for recognition. To conduct the experiments, 10000 samples for the contrast recognition and other relations were collected from the Corpus of Research Center of Intelligent Signal Processing as the data set. Then, feature vector was extracted from these samples. Finally, several supervised learning methods such as Supporting Vector Machine, the k-Nearest Neighbors algorithm, Parzen-Window, and the integration of these methods, were used to categorize and recognize contrast relations. The highest accuracy was 87. 13, which belonged to the combination of category-clauses in its best shape.