De-Conflated Semantic Representations
This addresses the issue of unreliable representations for infrequent word senses in natural language processing, offering a more accurate method for semantic modeling.
The paper tackles the problem of conflated word meanings in semantic representations by proposing a technique that de-conflates representations using a semantic network, achieving state-of-the-art results on most of six datasets across two semantic similarity tasks.
One major deficiency of most semantic representation techniques is that they usually model a word type as a single point in the semantic space, hence conflating all the meanings that the word can have. Addressing this issue by learning distinct representations for individual meanings of words has been the subject of several research studies in the past few years. However, the generated sense representations are either not linked to any sense inventory or are unreliable for infrequent word senses. We propose a technique that tackles these problems by de-conflating the representations of words based on the deep knowledge it derives from a semantic network. Our approach provides multiple advantages in comparison to the past work, including its high coverage and the ability to generate accurate representations even for infrequent word senses. We carry out evaluations on six datasets across two semantic similarity tasks and report state-of-the-art results on most of them.