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Part-of-Speech Tagging for Middle English through Alignment and Projection of Parallel Diachronic Texts

What it is about

The paper builds a Middle English tagger without hand-tagged data. Modern English text is tagged by a maximum-entropy tagger trained on the Wall Street Journal. The tags are projected to Middle English Wycliffe Bible text through word alignments of parallel verses. A bigram tagger cleans the projected tags, and a second maximum-entropy tagger is trained on the WSJ text and the cleaned Middle English text. On Biblical test text it reaches 84.8% accuracy for coarse tags, against a baseline of 63.4%. Other Middle English texts stay in the 60s. About 600 hand-labelled sentences were needed to match the best bootstrapped line.

Ideas

For Mova

The chapters are 09. Early Modern and archaic forms and 10. Word classes. The code is en/src/ptag.rs and tools/treeagree. The PPCME data is NC: use it only for tests. Measure first how many tokens of a verse-aligned lesson keep the same tag after projection.

Topics: Part-of-speech tagging

Cite

The paper itself, not this card. BibTeX from the library's record of the work.

@inproceedings{moon2007part,
  title = {{Part-of-Speech Tagging for Middle English through Alignment and Projection of Parallel Diachronic Texts}},
  author = {Moon, Taesun and Baldridge, Jason},
  booktitle = {Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)},
  year = {2007},
  url = {https://aclanthology.org/D07-1041/}
}

References

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