# Towards Semantic Noise Cleansing of Categorical Data based on Semantic Infusion

arXiv: https://arxiv.org/abs/2002.02238 · HTML: https://mlsanity.com/paper/2002.02238/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2020.

## Built on by (most cited first, 1 of 1)

- [SEMIE: SEMantically Infused Embeddings with Enhanced Interpretability for Domain-specific Small Corpus](https://mlsanity.com/paper/2103.11431/) (2021)

## Abstract (arXiv)

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.