Text segmentation with character-level text embeddings

TL;DR

This paper introduces a method to learn character-level text embeddings using a simple recurrent network, improving text segmentation tasks involving mixed natural language and code.

Contribution

It presents a novel approach to learn text representations directly from raw characters and demonstrates their effectiveness in a supervised segmentation task.

Findings

Embeddings significantly improve segmentation accuracy.

The method outperforms baseline surface n-gram features.

Learned representations capture useful structural information.

Abstract

Learning word representations has recently seen much success in computational linguistics. However, assuming sequences of word tokens as input to linguistic analysis is often unjustified. For many languages word segmentation is a non-trivial task and naturally occurring text is sometimes a mixture of natural language strings and other character data. We propose to learn text representations directly from raw character sequences by training a Simple recurrent Network to predict the next character in text. The network uses its hidden layer to evolve abstract representations of the character sequences it sees. To demonstrate the usefulness of the learned text embeddings, we use them as features in a supervised character level text segmentation and labeling task: recognizing spans of text containing programming language code. By using the embeddings as features we are able to substantially improve over a baseline which uses only surface character n-grams.