Learning Concept Abstractness Using Weak Supervision
Ella Rabinovich, Benjamin Sznajder, Artem Spector, Ilya Shnayderman,, Ranit Aharonov, David Konopnicki, Noam Slonim
TL;DR
This paper presents a weakly supervised method for determining the abstractness of words and expressions using minimal linguistic clues, achieving high correlation with human judgments without labeled data.
Contribution
It introduces a novel weak supervision technique for inferring concept abstractness from text, applicable across languages and resource-scarce settings.
Findings
High correlation with human labels for abstractness
Applicable to multiple languages and resource-scarce scenarios
Potential for inferring other concept properties
Abstract
We introduce a weakly supervised approach for inferring the property of abstractness of words and expressions in the complete absence of labeled data. Exploiting only minimal linguistic clues and the contextual usage of a concept as manifested in textual data, we train sufficiently powerful classifiers, obtaining high correlation with human labels. The results imply the applicability of this approach to additional properties of concepts, additional languages, and resource-scarce scenarios.
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Taxonomy
TopicsTopic Modeling · Text and Document Classification Technologies · Spam and Phishing Detection