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# A Rich Morphological Tagger for English: Exploring the Cross-Linguistic Tradeoff Between Morphology and Syntax

**Authors:** [Christo Kirov](https://md.mova.elamur.ai/free/en/docs/papers/authors/christo-kirov/), [John Sylak-Glassman](https://md.mova.elamur.ai/free/en/docs/papers/authors/john-sylak-glassman/), Rebecca Knowles, [Ryan Cotterell](https://md.mova.elamur.ai/free/en/docs/papers/authors/ryan-cotterell/), [Matt Post](https://md.mova.elamur.ai/free/en/docs/papers/authors/matt-post/) · **Year:** 2017 · **Venue:** Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers **Link:** [aclanthology.org/E17-2018](https://aclanthology.org/E17-2018/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**8** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/kirov-2017-rich-morphological-tagger-english-exploring/) of the 140673 papers whose bibliographies we read[Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers](https://aclanthology.org/volumes/E17-2/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/E17-2018/)
- [PDF](https://aclanthology.org/E17-2018.pdf)

**Authors (5)**

1. Christo Kirov
2. John Sylak-Glassman
3. Rebecca Knowles
4. Ryan Cotterell
5. Matt Post

Citations and author metrics from OpenAlex (); authors with the same name are not always told apart.

## What it is about

The question is whether English, a morphologically poor language, carries in its syntax the same meanings that Czech marks with inflection. The authors project rich morphological tags (UniMorph: case, number, person, tense, voice, and more) from Czech words onto their aligned English words in the Prague Czech–English treebank, using GIZA++ alignments and a rule for many-to-many links. Before training, they check the projections against PTB tags where both should agree (for example VBZ with third-person singular). A feed-forward network with two hidden layers and features from automatic dependency and CFG parses (head word and head POS, head chains, child determiners and case markers, distance to root) is trained on WSJ 02–21 and tested on WSJ 22. Whole-tag accuracy is 93.7%, against 14.1% for a most-frequent-value baseline. Case is the hardest subtag (69.0%, against 35.0% for the baseline), and dependency features help most for case.

## Ideas

- I1. `en/src/udreg.rs` and `en/src/morph.rs`: a rule-based Case feature read from the arcs. Give a noun Acc when it is a `obj` or `iobj` of a verb, Nom when it is `nsubj`, and the case of its `case` child otherwise. Report accuracy per class on UD EWT dev, against the majority baseline the paper uses.
- I2. Use the paper's split of features as a test design: report each subtag separately (case, number, tense, person) and never only the whole-tag score, so that a gain in one category is not hidden.
- I3. Negative control: shuffle the head labels within each sentence and re-run the same rule. The score must fall, or the rule is not reading the tree.
- I4. Projection check: for any cross-language projection Mova builds (for example from the Early Modern texts), check the projected value against the expected English form on words where both must agree, as the paper does with Table 2.

## For Mova

The chapters are [11. Nouns](https://md.mova.elamur.ai/free/en/docs/languages/en/11-nouns/) (case, number) and [13. Verbs](https://md.mova.elamur.ai/free/en/docs/languages/en/13-verbs/) (tense, person). The code is `en/src/udreg.rs` (the feature registry), `en/src/morph.rs`, and the parse output of `en/src/parse.rs`. The measure is per-feature accuracy on UD EWT dev, run twice to check determinism. The neural tagger is not for Mova; the feature design and the per-category baseline are.

Topics: [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/)

## Cite

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

```
@inproceedings{kirov2017rich,
  title = {{A Rich Morphological Tagger for English: Exploring the Cross-Linguistic Tradeoff Between Morphology and Syntax}},
  author = {Kirov, Christo and Sylak-Glassman, John and Knowles, Rebecca and Cotterell, Ryan and Post, Matt},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  year = {2017},
  url = {https://aclanthology.org/E17-2018/}
}
```

- [← previous From TreeBank to PropBank](https://md.mova.elamur.ai/free/en/docs/papers/kingsbury-2002-treebank-propbank/)
- [next → An Advanced English Grammar with Exercises](https://md.mova.elamur.ai/free/en/docs/papers/kittredge-1913-advanced-english-grammar-exercises/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

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