USAAR at SemEval-2016 Task 13: Hyponym Endocentricity

Abstract

This paper describes our submission to the SemEval-2016 Taxonomy Extraction Evaluation (TExEval-2) Task. We examine the endocentric nature of hyponyms and propose a simple rule-based method to identify hypernyms at high precision. For the food domain, we extract lists of terms from the Wikipedia lists of lists by using the name of each list as the endocentric head and treating all terms in the extracted tables as the hyponym of the endocentric head. Our submission achieved competitive results in taxonomy construction and ranked top in hypernym identification when evaluated against gold standard taxonomies and also in manual evaluation of novel relations not covered by the gold standard taxonomies.

SemEval-2016 Task 13: Taxonomy Extraction Evaluation (TExEval-2)

“…USAAR This system introduces hypernym endocentricity as a useful property for hypernym identification (Tan, 2016). Often multi-word hyponyms are endocentric constructions which contains a word that fulfills the same function as one part of its word.…”

Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings

“…Proposed methods are evaluated on the data of SemEval2016 TExEval (Bordea et al, 2016) for submitted systems that created taxonomies for all domains of the task 4 , namely the task-winning system TAXI (Panchenko et al, 2016) as well as the systems USAAR (Tan et al, 2016) and JUNLP (Maitra and Das, 2016). TAXI harvests hypernyms with substring inclusion and lexicalsyntactic patterns by obtaining domain-specific texts via focused web crawling.…”

Taxonomy Extraction for Customer Service Knowledge Base Construction

“…Although similar to hypernym learning [28], the challenges it proposes are quite different (see [10]). Multiple string and grammar-based methods have been proposed, where baseline systems have used string-based metrics with Hearst-like patterns learned from text [23], while more advanced ones have been based on the concept of endocentricity of terms to indicate a hypernym-like relationship [30]. Other methods not based on grammar such as genetic algorithms [14] or word embeddings [17,27] have also been explored.…”