Languages evolve over time. With new technical innovations, new terms get created and new senses are added to existing words. Dictionaries like WordNet which act as a database for English vocabulary should be updated with these new concepts. WordNet organizes these concepts in sets of synonyms and interlinks them by using semantic relations. Many Natural Language Processing applications like Machine Translation and Word Sense Disambiguation rely on WordNet for their functionality. WordNet was last updated in 2006. If WordNet is not updated with new vocabulary, the performance of applications which rely on WordNet would drop. The objective of our research is to automatically update WordNet with the new senses by using resources like online dictionaries and text corpora available over the internet. We use the ISA hierarchy structure of WordNet to insert new senses. In an ISA hierarchy, the concepts higher in a hierarchy (called hypernyms) are more abstract representations of the concepts lower in hierarchy (called hyponyms). To improve the coverage of our solution, we rely on two complementary techniques - traditional pattern matching and modern vector space models - to extract candidate hypernym from WordNet for a new sense. Our system was ranked 4 among the systems that participated in for this SemEval task SemEval 2016 Task 14 Semantic Taxonomy Enrichment. We also evaluate our system by participating in the task SemEval 2018 Task 09 Hypernym Discovery. In this task, we apply our system to the huge UMBC WebBase text corpus to extract candidate hypernyms for a given input term. Our system was ranked 3 among the systems which find hypernyms for Concepts.
University of Minnesota M.S. thesis.July 2018. Major: Computer Science. Advisor: Ted Pedersen. 1 computer file (PDF); ix, 126 pages.
Vallabhajosyula, Manikya Swathi.
Hypernym Discovery over WordNet and English Corpora - using Hearst Patterns and Word Embeddings.
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