DataWords: Getting Contrarian with Text, Structured Data and Explanations
Abstract
Our goal is to build classification models using a combination of free-text and structured data. To do this, we represent structured data by text sentences, DataWords, so that similar data items are mapped into the same sentence. This permits modeling a mixture of text and structured data by using only text-modeling algorithms. Several examples illustrate that it is possible to improve text classification performance by first running extraction tools (named entity recognition), then converting the output to DataWords, and adding the DataWords to the original text -- before model building and classification. This approach also allows us to produce explanations for inferences in terms of both free text and structured data.
Cite
@article{arxiv.2111.05384,
title = {DataWords: Getting Contrarian with Text, Structured Data and Explanations},
author = {Stephen I. Gallant and Mirza Nasir Hossain},
journal= {arXiv preprint arXiv:2111.05384},
year = {2022}
}
Comments
11 pages. Accepted for presentation at the Intelligent Systems Conference (IntelliSys) 2022, September 1-2, 2022, Amsterdam, The Netherlands