期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2017
卷号：2017
页码：324-330
语种：English
出版社：ACL Anthology
摘要：In this paper, we propose efficient and less resource-intensive strategies for parsing of code-mixed data. These strategies are not constrained by in-domain annotations, rather they leverage pre-existing monolingual annotated resources for training. We show that these methods can produce significantly better results as compared to an informed baseline. Due to lack of an evaluation set for code-mixed structures, we also present a data set of 450 Hindi and English code-mixed tweets of Hindi multilingual speakers for evaluation.
