Text segmentation with character-level text embeddings
This addresses text segmentation challenges in languages with non-trivial word boundaries or mixed data, though it is incremental as it builds on existing embedding methods.
The authors tackled the problem of text segmentation for mixed-language content by learning character-level text embeddings from raw sequences, which improved performance over a baseline using surface character n-grams in recognizing programming code spans.
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.