Malay Bhattacharyya
Markus Krause
ABSTRACT
Semantic similarity or semantic relatedness are features of natural language that contribute to the challenge machines face when analyzing text. Although semantic relatedness is still a complex challenge only few ground truth data set exist. We argue that the available corpora used to evaluate the performance of natural language tools do not capture all elements of the phenomenon. We present a set of simple interventions that illustrate 1) framing effects influence similarity perception, 2) the distribution of similarity across multiple users is important and 3) semantic relatedness is asymmetric.
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Index Terms
- Possible Confounds in Word-based Semantic Similarity Test Data
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