Number Entity Recognition

• URL: http://arxiv.org/abs/2205.03559v1
• Date: Sat, 7 May 2022 05:22:43 GMT
• Title: Number Entity Recognition
• Authors: Dhanasekar Sundararaman, Vivek Subramanian, Guoyin Wang, Liyan Xu, Lawrence Carin
• Abstract summary: Numbers are essential components of text, like any other word tokens, from which natural language processing (NLP) models are built and deployed. In this work, we attempt to tap this potential of state-of-the-art NLP models and transfer their ability to boost performance in related tasks. Our proposed classification of numbers into entities helps NLP models perform well on several tasks, including a handcrafted Fill-In-The-Blank (FITB) task and on question answering using joint embeddings.
• Score: 65.80137628972312
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Numbers are essential components of text, like any other word tokens, from which natural language processing (NLP) models are built and deployed. Though numbers are typically not accounted for distinctly in most NLP tasks, there is still an underlying amount of numeracy already exhibited by NLP models. In this work, we attempt to tap this potential of state-of-the-art NLP models and transfer their ability to boost performance in related tasks. Our proposed classification of numbers into entities helps NLP models perform well on several tasks, including a handcrafted Fill-In-The-Blank (FITB) task and on question answering using joint embeddings, outperforming the BERT and RoBERTa baseline classification.

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