UMDuluth-CS8761 at SemEval-2018 Task 9: Hypernym Discovery using Hearst Patterns, Co-occurrence frequencies and Word Embeddings
This work addresses hypernym discovery for natural language processing, but it is incremental as it combines existing methods without major innovation.
The paper tackled hypernym discovery by combining co-occurrence frequencies, Hearst patterns, and word embeddings, achieving 6th out of 19 systems for concept hypernyms and 12th out of 18 for entity hypernyms in a SemEval competition.
Hypernym Discovery is the task of identifying potential hypernyms for a given term. A hypernym is a more generalized word that is super-ordinate to more specific words. This paper explores several approaches that rely on co-occurrence frequencies of word pairs, Hearst Patterns based on regular expressions, and word embeddings created from the UMBC corpus. Our system Babbage participated in Subtask 1A for English and placed 6th of 19 systems when identifying concept hypernyms, and 12th of 18 systems for entity hypernyms.