---
title: "A Sentence Judgment System for Grammatical Error — Lee 2014"
description: "Learners of Chinese as a foreign language (CFL) have far fewer automatic grammar-checking tools than learners of English, even as MOOCs increase the need…"
authors: ["Lung-Hao Lee", "Liang-Chih Yu", "Kuei-Ching Lee", "Yuen-Hsien Tseng", "Li-Ping Chang", "Hsin-Hsi Chen"]
year: 2014
venue: "Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: System Demonstrations"
source: "https://aclanthology.org/C14-2015/"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/lee-2014-sentence-judgment-system-grammatical-error/"
---
[Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/lee-2014-sentence-judgment-system-grammatical-error/)

[home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2014](https://md.research.elamur.ai/free/papers/2014/) / A Sentence Judgment System for Grammatical Error Detection

# A Sentence Judgment System for Grammatical Error Detection

**Authors:** Lung-Hao Lee, Liang-Chih Yu, Kuei-Ching Lee, Yuen-Hsien Tseng, Li-Ping Chang, [Hsin-Hsi Chen](https://md.research.elamur.ai/free/authors/hsin-hsi-chen/) · **Year:** 2014 · **Venue:** Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: System Demonstrations · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/C14-2015/) · [PDF](https://aclanthology.org/C14-2015.pdf) · [OpenAlex](https://openalex.org/W2251972200)

**Citations:** 35 citations (openalex-snapshot · 2026-10-14) · 16 in our library, of the 140673 papers whose bibliographies we read

## Summary
Learners of Chinese as a foreign language (CFL) have far fewer automatic grammar-checking tools than learners of English, even as MOOCs increase the need for large-scale assessment. The authors present a web-based demo system that decides whether a Chinese sentence contains grammatical errors. Each input is segmented, with unknown-word merging to handle OOV items, and POS-tagged. Two detectors then analyse it. The first applies 142 expert-written syntactic rules derived from Cheng's (1997) collection of 900 typical learner errors. The second is a statistical component. Bi- and tri-gram SRILM models trained on the Sinica corpus score 19,080 erroneous HSK sentences and their manual corrections, and normal densities fitted to these scores act as "correct" and "incorrect" models. A sentence is flagged when the incorrect model gives it higher probability. In tests, the rules proved precise but missed most errors, while the n-gram models showed the opposite pattern. Combining the two lets users trade recall against precision. Because the interface also displays the matched rules and n-gram frequencies, it can support autonomous learning.

## Key points
- The rule notation uses wildcards, exclusions, alternatives, mutual exclusivity (mx{W1 W2}) and a follows-after operator over POS tags.
- The test set has 880 erroneous sentences from CSL learners at the NCKU Chinese Language Center, plus their 880 corrected versions.
- Rule-based detection reached a precision of 0.857 but a recall of only 0.224, with a false positive rate of 0.038.
- The 3-gram model outperformed the 2-gram model on every metric (F1 0.689 vs 0.638), but it wrongly flagged about 60% of correct sentences.
- Combining rules with AND raised precision to 0.924 and lowered FPR to 0.007, though recall fell to 0.083. The OR combination reached a recall of 1.0. The demo itself flags a sentence only when both methods agree.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

## Cite

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

```bibtex
@inproceedings{lee2014sentence,
  title = {{A Sentence Judgment System for Grammatical Error Detection}},
  author = {Lee, Lung-Hao and Yu, Liang-Chih and Lee, Kuei-Ching and Tseng, Yuen-Hsien and Chang, Li-Ping and Chen, Hsin-Hsi},
  booktitle = {Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: System Demonstrations},
  year = {2014},
  url = {https://aclanthology.org/C14-2015/}
}
```

- [← previous A Rule-Based System for Unrestricted Bridging Resolution: Recognizing Bridging Anaphora and Finding Links to Antecedents](https://md.research.elamur.ai/free/papers/hou-2014-rule-based-system-unrestricted-bridging/)
- [next → A Systematic Comparison of Smoothing Techniques for Sentence-Level BLEU](https://md.research.elamur.ai/free/papers/chen-2014-systematic-comparison-smoothing-techniques-sentence/)
- [↑ back to papers of 2014](https://md.research.elamur.ai/free/papers/2014/)

## References

9 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Andreas Stolcke. 2002. SRILM — An extensible language modeling toolkit. Proceedings of ICSLP’02, pages 901-904. [find](https://scholar.google.com/scholar?q=Andreas+Stolcke%2E+2002%2E+SRILM+%E2%80%94+An+extensible+language+modeling+toolkit%2E+Proceedings+of+ICSLP%E2%80%9902%2C+pages+901%2D904%2E)
2. Chi-Hsin Yu and Hsin-Hsi Chen. 2012. Detecting word ordering errors in Chinese sentences for learning Chinese as a foreign language. Proceedings of COLING’12, pages 3003-3018. [find](https://scholar.google.com/scholar?q=Chi%2DHsin+Yu+and+Hsin%2DHsi+Chen%2E+2012%2E+Detecting+word+ordering+errors+in+Chinese+sentences+for+learning+Chinese+as+a+foreign+language%2E+Proceedings+of+COLING%E2%80%9912%2C+pages+3003%2D3018%2E)
3. Christopher D. Manning and Hinrich Schütze. 1999. Foundations of Statistical Natural Language Processing. MIT Press. Cambridge, MA. [find](https://scholar.google.com/scholar?q=Christopher+D%2E+Manning+and+Hinrich+Sch%C3%BCtze%2E+1999%2E+Foundations+of+Statistical+Natural+Language+Processing%2E+MIT+Press%2E+Cambridge%2C+MA%2E)
4. Chung-Hsien Wu, Chao-Hung Liu, Matthew Harris and Liang-Chih Yu. 2010. Sentence correction incorporating relative position and parse template language model. IEEE Transactions on Audio, Speech, and Language Processing, 18(6):1170-1181. [find](https://scholar.google.com/scholar?q=Chung%2DHsien+Wu%2C+Chao%2DHung+Liu%2C+Matthew+Harris+and+Liang%2DChih+Yu%2E+2010%2E+Sentence+correction+incorporating+relative+position+and+parse+template+language+model%2E+IEEE+Transactions+on+Audio%2C+Speech%2C+and+La)
5. Keh-Jiann Chen and Wei-Yun Ma. 2002. Unknown word extraction for Chinese documents. Proceedings of COLING’02, pages 169-175. [find](https://scholar.google.com/scholar?q=Keh%2DJiann+Chen+and+Wei%2DYun+Ma%2E+2002%2E+Unknown+word+extraction+for+Chinese+documents%2E+Proceedings+of+COLING%E2%80%9902%2C+pages+169%2D175%2E)
6. M. Cheng. 1997. Analysis of 900 Common Erroneous Samples of Chinese Sentences - for Chinese Learners from English Speaking Countries (in Chinese). Beijing, CN: Sinolingua. [find](https://scholar.google.com/scholar?q=M%2E+Cheng%2E+1997%2E+Analysis+of+900+Common+Erroneous+Samples+of+Chinese+Sentences+%2D+for+Chinese+Learners+from+English+Speaking+Countries+%28in+Chinese%29%2E+Beijing%2C+CN%3A+Sinolingua%2E)
7. Ru-Ying Chang, Chung-Hsien Wu, and Philips K. Prasetyo. 2012. Error diagnosis of Chinese sentences using inductive learning algorithm and decomposition-based testing mechanism. ACM Transactions on Asian Language Information Processing, 11(1):Article 3. [find](https://scholar.google.com/scholar?q=Ru%2DYing+Chang%2C+Chung%2DHsien+Wu%2C+and+Philips+K%2E+Prasetyo%2E+2012%2E+Error+diagnosis+of+Chinese+sentences+using+inductive+learning+algorithm+and+decomposition%2Dbased+testing+mechanism%2E+ACM+Transactions+on+Asi)
8. Yu-Fang Tsai and Keh-Jiann Chen. 2004. Reliable and cost-effective pos-tagging. International Journal of Computational Linguistics and Chinese Language Processing, 9(1):83-96. [find](https://scholar.google.com/scholar?q=Yu%2DFang+Tsai+and+Keh%2DJiann+Chen%2E+2004%2E+Reliable+and+cost%2Deffective+pos%2Dtagging%2E+International+Journal+of+Computational+Linguistics+and+Chinese+Language+Processing%2C+9%281%29%3A83%2D96%2E)
9. Recall 0.224 0.751 0.838 1.000 1.000 0.083 0.083 F1 0.356 0.638 0.689 0.667 0.668 0.153 0.153 False Positive Rate 0.038 0.603 0.595 1.000 0.993 0.007 0.007 [find](https://scholar.google.com/scholar?q=Recall+0%2E224+0%2E751+0%2E838+1%2E000+1%2E000+0%2E083+0%2E083+F1+0%2E356+0%2E638+0%2E689+0%2E667+0%2E668+0%2E153+0%2E153+False+Positive+Rate+0%2E038+0%2E603+0%2E595+1%2E000+0%2E993+0%2E007+0%2E007)