[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/finch-2006-lexical-dependency-ontological-knowledge-improve/)

[home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [English](https://md.mova.elamur.ai/free/en/docs/papers/en/) / Using Lexical Dependency and Ontological Knowledge to Improve a Detailed Syntactic and Semantic Tagger of English

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

**Authors:** Andrew Finch, Ezra Black, Young-Sook Hwang, Eiichiro Sumita · **Year:** 2006 · **Venue:** Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions **Link:** [aclanthology.org/P06-2028](https://aclanthology.org/P06-2028/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — summary only

[**3** citations](https://openalex.org/works?filter=cites:W2139576577) openalex-snapshot · 2026-10-14[Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions](https://aclanthology.org/volumes/P06-2/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/P06-2028/)
- [PDF](https://aclanthology.org/P06-2028.pdf)
- [DOI 10.3115/1273073.1273101](https://doi.org/10.3115/1273073.1273101)
- [OpenAlex](https://openalex.org/W2139576577)

**Authors (4)**

1. [Andrew Finch](https://openalex.org/A5081198426) [ORCID](https://orcid.org/0000-0002-7067-7098) · [Semantic Scholar](https://www.semanticscholar.org/author/2987548) · h 16 · 1003 citations · 98 works
2. [Ezra Black](https://openalex.org/A5111744611) [Semantic Scholar](https://www.semanticscholar.org/author/1818123) · h 14 · 944 citations · 45 works
3. [Young-Sook Hwang](https://openalex.org/A5006643967) [Semantic Scholar](https://www.semanticscholar.org/author/35009674) · h 11 · 504 citations · 35 works
4. [Eiichiro Sumita](https://openalex.org/A5033318800) [ORCID](https://orcid.org/0000-0002-1028-4399) · [Semantic Scholar](https://www.semanticscholar.org/author/1698363) · h 38 · 6223 citations · 447 works

Citations and author metrics from openalex-snapshot (2026-10-14); authors with the same name are not always told apart.

## 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](https://md.mova.elamur.ai/free/en/docs/languages/en/10-word-classes/) (tag classes) and [19. The lexicon: vocabulary, multiword expressions and register](https://md.mova.elamur.ai/free/en/docs/languages/en/19-lexicon-mwe-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](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. [DOI 10.3115/1273073.1273101](https://doi.org/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}
}
```

- [← previous Tree Linearization in English: Improving Language Model Based Approaches](https://md.mova.elamur.ai/free/en/docs/papers/filippova-2009-tree-linearization-english-improving-language/)
- [next → A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples](https://md.mova.elamur.ai/free/en/docs/papers/findings-2021-wang-closer-look-into-robustness-neural/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

21 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. E. Black and A. Finch. 2001. Developing and proving effective broad-coverage semantic-and-syntactic tagsets for natural language: The atr approach. In Proceedings of ICCPOL-2001. [find](https://scholar.google.com/scholar?q=E%2E+Black+and+A%2E+Finch%2E+2001%2E+Developing+and+proving+effective+broad%2Dcoverage+semantic%2Dand%2Dsyntactic+tagsets+for+natural+language%3A+The+atr+approach%2E+In+Proceedings+of+ICCPOL%2D2001%2E)
2. E. Black, S. Eubank, H. Kashioka, R. Garside, G. Leech, and D. Magerman. 1996a. Beyond skeleton parsing: producing a comprehensive large– scale general–english treebank with full grammatical analysis. In Proceedings of the 16th Annual Conference on Computational Linguistics, pages 107– 112, Copenhagen. [find](https://scholar.google.com/scholar?q=E%2E+Black%2C+S%2E+Eubank%2C+H%2E+Kashioka%2C+R%2E+Garside%2C+G%2E+Leech%2C+and+D%2E+Magerman%2E+1996a%2E+Beyond+skeleton+parsing%3A+producing+a+comprehensive+large%E2%80%93+scale+general%E2%80%93english+treebank+with+full+grammatical+analysis%2E)
3. E. Black, S. Eubank, H. Kashioka, and J. Saia. 1996b. Reinventing part-of-speech tagging. Journal of Natural Language Processing (Japan), 5:1. [find](https://scholar.google.com/scholar?q=E%2E+Black%2C+S%2E+Eubank%2C+H%2E+Kashioka%2C+and+J%2E+Saia%2E+1996b%2E+Reinventing+part%2Dof%2Dspeech+tagging%2E+Journal+of+Natural+Language+Processing+%28Japan%29%2C+5%3A1%2E)
4. Ezra Black, Andrew Finch, and Hideki Kashioka. 1998. Trigger-pair predictors in parsing and tagging. In Proceedings, 36th Annual Meeting of the Association for Computational Linguistics, 17th Annual Conference on Computational Linguistics, Montreal, Canada. [find](https://scholar.google.com/scholar?q=Ezra+Black%2C+Andrew+Finch%2C+and+Hideki+Kashioka%2E+1998%2E+Trigger%2Dpair+predictors+in+parsing+and+tagging%2E+In+Proceedings%2C+36th+Annual+Meeting+of+the+Association+for+Computational+Linguistics%2C+17th+Annual+C)
5. Michael John Collins. 1996. A new statistical parser based on bigram lexical dependencies. In Arivind Joshi and Martha Palmer, editors, Proceedings of the Thirty-Fourth Annual Meeting of the Association for Computational Linguistics, pages 184–191, San Francisco. Morgan Kaufmann Publishers. [find](https://scholar.google.com/scholar?q=Michael+John+Collins%2E+1996%2E+A+new+statistical+parser+based+on+bigram+lexical+dependencies%2E+In+Arivind+Joshi+and+Martha+Palmer%2C+editors%2C+Proceedings+of+the+Thirty%2DFourth+Annual+Meeting+of+the+Associati)
6. C. Fellbaum. 1998. WordNet: An Electronic Lexical Database. MIT Press. [find](https://scholar.google.com/scholar?q=C%2E+Fellbaum%2E+1998%2E+WordNet%3A+An+Electronic+Lexical+Database%2E+MIT+Press%2E)
7. Dennis Grinberg, John Lafferty, and Daniel Sleator. 1995. A robust parsing algorithm for LINK grammars. Technical Report CMU-CS-TR-95-125, CMU, Pittsburgh, PA. [find](https://scholar.google.com/scholar?q=Dennis+Grinberg%2C+John+Lafferty%2C+and+Daniel+Sleator%2E+1995%2E+A+robust+parsing+algorithm+for+LINK+grammars%2E+Technical+Report+CMU%2DCS%2DTR%2D95%2D125%2C+CMU%2C+Pittsburgh%2C+PA%2E)
8. J. Kupiec. 1992. Robust part-of-speech tagging using a hidden markov model. Computer Speech and Language, 6:225–242. [↗](https://doi.org/10.1016/0885-2308(92)90019-z)
9. A. K. Lamjiri, O. El Demerdash, and L.Kosseim. 2004. Simple features for statistical word sense disambiguation. In Proc. ACL 2004 – Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text (Senseval-3), Barcelona, Spain, July. ACL-2004. OOV’s 30.58 30.92 30.92 31.24 35.08 36.18 37.02 Nouns 68.47 68.18 69.16 69.36 71.89 72.28 72.80 Verbs 74.32 74.96 77.21 75.78 75.83 76.29 78.36 Adj/Adv 70.99 73.02 73.52 72.62 75.34 74.47 76.16 [find](https://scholar.google.com/scholar?q=A%2E+K%2E+Lamjiri%2C+O%2E+El+Demerdash%2C+and+L%2EKosseim%2E+2004%2E+Simple+features+for+statistical+word+sense+disambiguation%2E+In+Proc%2E+ACL+2004+%E2%80%93+Third+International+Workshop+on+the+Evaluation+of+Systems+for+the+Se)
10. C. Li and H. Li. 2002. Word translation disambiguation using bilingual bootstrapping. [find](https://scholar.google.com/scholar?q=C%2E+Li+and+H%2E+Li%2E+2002%2E+Word+translation+disambiguation+using+bilingual+bootstrapping%2E)
11. Xiaobin Li, Stan Szpakowicz, and Stan Matwin. 1995. A wordnet-based algorithm for word sense disambiguation. In IJCAI, pages 1368–1374. [find](https://scholar.google.com/scholar?q=Xiaobin+Li%2C+Stan+Szpakowicz%2C+and+Stan+Matwin%2E+1995%2E+A+wordnet%2Dbased+algorithm+for+word+sense+disambiguation%2E+In+IJCAI%2C+pages+1368%E2%80%931374%2E)
12. Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz. 1994. Building a large annotated corpus of english: The penn treebank. Computational Linguistics, 19(2):313–330. [↗](https://doi.org/10.21236/ada273556) [our card](https://md.mova.elamur.ai/free/en/docs/papers/bank-1993-marcus-building-large-annotated-corpus-english/)
13. B. Merialdo. 1994. Tagging english text with a probabilistic model. Computational Linguistics, 20(2):155–172. [find](https://scholar.google.com/scholar?q=B%2E+Merialdo%2E+1994%2E+Tagging+english+text+with+a+probabilistic+model%2E+Computational+Linguistics%2C+20%282%29%3A155%E2%80%93172%2E)
14. Rada Mihalcea and Dan I. Moldovan. 1998. Word sense disambiguation based on semantic density. In Sanda Harabagiu, editor, Use of WordNet in Natural Language Processing Systems: Proceedings of the Conference, pages 16–22. Association for Computational Linguistics, Somerset, New Jersey. [find](https://scholar.google.com/scholar?q=Rada+Mihalcea+and+Dan+I%2E+Moldovan%2E+1998%2E+Word+sense+disambiguation+based+on+semantic+density%2E+In+Sanda+Harabagiu%2C+editor%2C+Use+of+WordNet+in+Natural+Language+Processing+Systems%3A+Proceedings+of+the+Conf)
15. I. Nancy and V. Jean. 1998. Word sense disambiguation: The state of the art. Computational Linguistics, 24:1:1–40. [find](https://scholar.google.com/scholar?q=I%2E+Nancy+and+V%2E+Jean%2E+1998%2E+Word+sense+disambiguation%3A+The+state+of+the+art%2E+Computational+Linguistics%2C+24%3A1%3A1%E2%80%9340%2E)
16. G. Ramakrishnan and B. Prithviraj. 2004. Soft word sense disambiguation. In International Conference on Global Wordnet (GWC 04), Brno, Czeck Republic. [find](https://scholar.google.com/scholar?q=G%2E+Ramakrishnan+and+B%2E+Prithviraj%2E+2004%2E+Soft+word+sense+disambiguation%2E+In+International+Conference+on+Global+Wordnet+%28GWC+04%29%2C+Brno%2C+Czeck+Republic%2E)
17. A. Ratnaparkhi. 1996. A maximum entropy part-of-speech tagger. In Proceedings of the Empirical Methods in Natural Language Processing Conference. [find](https://scholar.google.com/scholar?q=A%2E+Ratnaparkhi%2E+1996%2E+A+maximum+entropy+part%2Dof%2Dspeech+tagger%2E+In+Proceedings+of+the+Empirical+Methods+in+Natural+Language+Processing+Conference%2E)
18. R. Rosenfeld. 1996. A maximum entropy approach to adaptive statistical language modelling. Computer Speech and Language, 10:187–228. [↗](https://doi.org/10.1006/csla.1996.0011)
19. A. Suarez. 2002. A maximum entropy-based word sense disambiguation system. In Proc. International Conference on Computational Linguistics. [find](https://scholar.google.com/scholar?q=A%2E+Suarez%2E+2002%2E+A+maximum+entropy%2Dbased+word+sense+disambiguation+system%2E+In+Proc%2E+International+Conference+on+Computational+Linguistics%2E)
20. A. Ushioda. 1996. Hierarchical clustering of words. In In Proceedings of COLING 96, pages 1159–1162. [find](https://scholar.google.com/scholar?q=A%2E+Ushioda%2E+1996%2E+Hierarchical+clustering+of+words%2E+In+In+Proceedings+of+COLING+96%2C+pages+1159%E2%80%931162%2E)
21. D. Yarowsky. 1993. One sense per collocation. In In the Proceedings of ARPA Human Language Technology Workshop. [↗](https://doi.org/10.21236/ada458621)