Duluth at SemEval--2016 Task 14 : Extending Gloss Overlaps to Enrich Semantic Taxonomies
This work addresses a domain-specific challenge in natural language processing for improving semantic taxonomies, but it is incremental as it builds on existing gloss overlap methods.
The paper tackled the problem of enriching semantic taxonomies by identifying synonyms between WordNet and other dictionaries using gloss overlaps, achieving results better than a random baseline and close to the median in a SemEval task.
This paper describes the Duluth systems that participated in Task 14 of SemEval 2016, Semantic Taxonomy Enrichment. There were three related systems in the formal evaluation which are discussed here, along with numerous post--evaluation runs. All of these systems identified synonyms between WordNet and other dictionaries by measuring the gloss overlaps between them. These systems perform better than the random baseline and one post--evaluation variation was within a respectable margin of the median result attained by all participating systems.