Letter Sequence Labeling for Compound Splitting

Abstract

For languages such as German where compounds occur frequently and are written as single tokens, a wide variety of NLP applications benefits from recognizing and splitting compounds. As the traditional word frequency-based approach to compound splitting has several drawbacks, this paper introduces a letter sequence labeling approach, which can utilize rich word form features to build discriminative learning models that are optimized for splitting. Experiments show that the proposed method significantly outperforms state-of-the-art compound splitters.