Nonsymbolic Text Representation

TL;DR

This paper presents a novel nonsymbolic text representation model that does not rely on segmentation or tokenization, outperforming previous methods in information extraction and text denoising tasks.

Contribution

It introduces the first generic nonsymbolic text representation model applicable without segmentation, advancing the capabilities of text processing.

Findings

Outperforms prior models in information extraction

Achieves better results in text denoising

Operates without segmentation or tokenization

Abstract

We introduce the first generic text representation model that is completely nonsymbolic, i.e., it does not require the availability of a segmentation or tokenization method that attempts to identify words or other symbolic units in text. This applies to training the parameters of the model on a training corpus as well as to applying it when computing the representation of a new text. We show that our model performs better than prior work on an information extraction and a text denoising task.