Part of speech tagging for code switched data
This addresses the challenge of processing intra-sentential code-switching in NLP, which is incremental as it applies existing methods to a specific linguistic problem.
The paper tackles part-of-speech tagging for code-switched data, comparing strategies like using two monolingual taggers versus a unified tagger, and finds that applying two state-of-the-art POS taggers yields the best performance.
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.