Using Lexical Dependency and Ontological Knowledge to Improve a Detailed Syntactic and Semantic Tagger of English

What it is about

The paper tags each word with a syntactic tag and a broad semantic class at once, using a maximum-entropy tagger with a beam search from left to right. The data is the ATR General English Treebank, about 518,000 words with a tagset of roughly 165 syntactic tags and 42 plus 29 semantic classes for nouns, adjectives, adverbs and verbs. Two extra knowledge sources are added as binary features: lexical dependencies from a parser (a link-grammar parser and Collins' phrase-structure parser), and the position of a word in a hierarchy, either WordNet hypernyms or an automatic clustering of 50 million words. The best model reaches 84.90% accuracy, against an 82.58% baseline, and raises accuracy on words unseen in training (OOV) from 30.58% to 37.02%. Dependency features help most on verbs, and the ontology features help most on OOV words; the two gains add up.

Ideas

• I1. en/src/ptag.rs and en/src/tag.rs: add a feature from the head and dependents of each word, taken from a UD parse of the same sentence, as a second pass. Keep the feature order in BTreeMap form, and measure UPOS and feature accuracy on UD EWT dev, run twice.
• I2. Use a hypernym chain from world/src/dictionary.rs as back-off features for words the tagger has not seen. Report OOV accuracy separately, because the paper shows that this is where the ontology helps.
• I3. Negative control: shuffle the dependency labels in the features. If the gain stays, the feature is not carrying the signal the paper claims.

For Mova

The chapters are 10. Word classes (tag classes) and 19. The lexicon: vocabulary, multiword expressions and register (lexicon and senses). The code is en/src/ptag.rs, en/src/tag.rs and world/src/dictionary.rs. The measures: tag and feature accuracy on UD EWT dev, the OOV split, and the same run twice with identical output. The paper's maximum-entropy model is not an idea for Mova; the feature design and the OOV back-off are.

Topics: Part-of-speech tagging

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.3115/1273073.1273101

@inproceedings{finch2006using,
  title = {{Using Lexical Dependency and Ontological Knowledge to Improve a Detailed Syntactic and Semantic Tagger of English}},
  author = {Finch, Andrew and Black, Ezra and Hwang, Young-Sook and Sumita, Eiichiro},
  booktitle = {Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions},
  year = {2006},
  doi = {10.3115/1273073.1273101},
  url = {https://doi.org/10.3115/1273073.1273101}
}

References

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