Towards Semantic Noise Cleansing of Categorical Data based on Semantic Infusion

• URL: http://arxiv.org/abs/2002.02238v1
• Date: Thu, 6 Feb 2020 13:11:46 GMT
• Title: Towards Semantic Noise Cleansing of Categorical Data based on Semantic Infusion
• Authors: Rishabh Gupta and Rajesh N Rao
• Abstract summary: We formalize semantic noise as a sequence of terms that do not contribute to the narrative of the text. We present a novel Semantic Infusion technique to associate meta-data with the categorical corpus text. We propose an unsupervised text-preprocessing framework to filter the semantic noise using the context of the terms.
• Score: 4.825584239754082
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Semantic Noise affects text analytics activities for the domain-specific industries significantly. It impedes the text understanding which holds prime importance in the critical decision making tasks. In this work, we formalize semantic noise as a sequence of terms that do not contribute to the narrative of the text. We look beyond the notion of standard statistically-based stop words and consider the semantics of terms to exclude the semantic noise. We present a novel Semantic Infusion technique to associate meta-data with the categorical corpus text and demonstrate its near-lossless nature. Based on this technique, we propose an unsupervised text-preprocessing framework to filter the semantic noise using the context of the terms. Later we present the evaluation results of the proposed framework using a web forum dataset from the automobile-domain.

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