We address the problem of Part of Speech tagging (POS) in the context of
linguistic code switching (CS). CS is the phenomenon where a speaker switches
between two languages or variants of the same language within or across
utterances, known as intra-sentential or inter-sentential CS, respectively.
Processing CS data is especially challenging in intra-sentential data given
state of the art monolingual NLP technology since such technology is geared
toward the processing of one language at a time. In this paper we explore
multiple strategies of applying state of the art POS taggers to CS data. We
investigate the landscape in two CS language pairs, Spanish-English and Modern
Standard Arabic-Arabic dialects. We compare the use of two POS taggers vs. a
unified tagger trained on CS data. Our results show that applying a machine
learning framework using two state of the art POS taggers achieves better
performance compared to all other approaches that we investigate.

本文研究多种策略用于在两种语言或方言之间进行 linguistic code switching POS 标注的方法，并表明使用两种现有的 POS 标注器的机器学习框架比其他方法实现的性能更好。