Toward an Unsupervised Method for Assessing Semantic Specificity
Abstract: In the rapidly evolving field of natural language processing (NLP), classifying and understanding semantic specificity or "scope" is increasingly crucial. The ability to discern whether textual content is "general" or "specific" benefits advanced systems, such as recommendation engines, to deliver more targeted and relevant content or other NLP systems that require nuanced understanding of text. Although classifying scope is an important topic, there is minimal research on how to utilize NLP techniques to determine scope.
The incorporation of unsupervised learning methods in determining textual scope presents a novel approach within the NLP field. This paper expands the use of unsupervised techniques to classify text according to its semantic specificity without directly relying on vast amounts of pre-labeled data. By embedding textual data and applying heuristic clustering based on linguistic and syntactic cues, the methodology in this paper addresses the absence of direct unsupervised methods for classifying the "scope" of sentences or paragraphs.
Paper Type: Short
Research Area: Semantics: Lexical and Sentence-Level
Research Area Keywords: natural language inference, semantic textual similarity, phrase/sentence embedding
Contribution Types: Approaches to low-resource settings
Languages Studied: english
Submission Number: 2959
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