Table of Contents
Big Data Cogn. Comput., Volume 3, Issue 2 (June 2019) – 15 articles
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Cover Story (view full-size image) Word embeddings have been successful in many natural language processing tasks, although they [...] Read more. Word embeddings have been successful in many natural language processing tasks, although they characterize the meaning of a word by uninterpretable “context signatures”. Such a representation can render the results obtained using embeddings as difficult to interpret. Neighboring word vectors may have similar meanings, but in what way are they similar? That similarity may represent a synonymy, metonymy, or even antonymy relation. In the cognitive psychology literature, in contrast, concepts are frequently represented by their relations with properties. These properties are produced by test subjects when asked to describe the important features of concepts. As such, they form a natural, intuitive feature space. In this work, we present a neural network-based method for mapping a distributional semantic space onto a human-built property space automatically. View this paper.