Universal Word Segmentation: Implementation and Interpretation

Yan Shao, Christian Hardmeier, Joakim Nivre

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Word segmentation is a low-level NLP task that is non-trivial for a considerable number of languages. In this paper, we present a sequence tagging framework and apply it to word segmentation for a wide range of languages with different writing systems and typological characteristics. Additionally, we investigate the correlations between various typological factors and word segmentation accuracy. The experimental results indicate that segmentation accuracy is positively related to word boundary markers and negatively to the number of unique non-segmental terms. Based on the analysis, we design a small set of language-specific settings and extensively evaluate the segmentation system on the Universal Dependencies datasets. Our model obtains state-of-the-art accuracies on all the UD languages. It performs substantially better on languages that are non-trivial to segment, such as Chinese, Japanese, Arabic and Hebrew, when compared to previous work.
Original languageEnglish
Pages (from-to)421-435
Number of pages15
JournalTransactions of the Association for Computational Linguistics
Volume6
DOIs
Publication statusPublished - Jul 2018

Fingerprint

Dive into the research topics of 'Universal Word Segmentation: Implementation and Interpretation'. Together they form a unique fingerprint.

Cite this