Learning a POS tagger for AAVE-like language

@inproceedings{Jrgensen2016LearningAP,
  title={Learning a POS tagger for AAVE-like language},
  author={Anna J\orgensen and Dirk Hovy and Anders S\ogaard},
  booktitle={HLT-NAACL},
  year={2016}
}
Part-of-speech (POS) taggers trained on newswire perform much worse on domains such as subtitles, lyrics, or tweets. In addition, these domains are also heterogeneous, e.g., with respect to registers and dialects. In this paper, we consider the problem of learning a POS tagger for subtitles, lyrics, and tweets associated with African-American Vernacular English (AAVE). We learn from a mixture of randomly sampled and manually annotated Twitter data and unlabeled data, which we automatically and… CONTINUE READING

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