Using distributional semantics for automatic taxonomy induction

Semantic taxonomies are powerful tools that provide structured knowledge to Natural Language Processing (NLP), Information Retreval (IR), and general Artificial Intelligence (AI) systems. These taxonomies are extensively used for solving knowledge rich problems such as textual entailment and question answering. In this paper, we present a taxonomy induction system and evaluate it using the benchmarks provided in the Taxonomy Extraction Evaluation (TExEval2) Task. The task is to identify hyponym-hypernym relations and to construct a taxonomy from a given domain specific list. Our approach is based on a word embedding, trained from a large corpus and string-matching approaches. The overall approach is semi-supervised. We propose a generic algorithm that utilizes the vectors from the embedding effectively, to identify hyponym-hypernym relations and to induce the taxonomy. The system generated taxonomies on English language for three different domains (environment, food and science) which are evaluated against gold standard taxonomies. The system achieved good results for hyponym-hypernym identification and taxonomy induction, especially when compared to other tools using similar background knowledge.