A cascaded framework for identification and extraction of antonym for Turkish language

Abstract

Identification and extraction of semantic relations are challenging tasks in Natural Language Processing. In this paper, we design and propose three different models for the two separate tasks of identifying and extracting antonyms. In the first model, we develop two methods to identify antonyms: the first method consists of a probabilistic approach to calculate the probability of a given target/candidate pair being an antonym, whereby two distinct scoring functions are proposed to decide about the correct candidate for each target word; the second method consists of learning word embeddings and measuring embedding similarity to identify antonym pairs. In the second proposed model, we represent target/candidate pairs by a set of features that are compatible with those that are used by a supervised machine learning algorithm. The first and second models both especially well-suited for the identification of antonymy. In the last and third model, we adopt a minimally supervised bootstrapping approach, which operates by starting with a few antonym pairs and producing, thereafter, both seeds and patterns in an iterative fashion. Our study is deemed to be a significant contribution toward enriching the lexicon of the Turkish language.