---
title: "A Probabilistic Method for Analyzing Japanese — Seki 2002"
description: "Japanese often omits obligatory arguments of verbs."
authors: ["Kazuhiro Seki", "Atsushi Fujii", "Tetsuya Ishikawa"]
year: 2002
venue: "COLING 2002: The 19th International Conference on Computational Linguistics"
source: "https://aclanthology.org/C02-1078/"
arxiv: "cs/0206030"
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/en/papers/seki-2002-probabilistic-method-analyzing-japanese-anaphora/"
---
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# A Probabilistic Method for Analyzing Japanese Anaphora Integrating Zero Pronoun Detection and Resolution

**Authors:** Kazuhiro Seki, Atsushi Fujii, Tetsuya Ishikawa · **Year:** 2002 · **Venue:** COLING 2002: The 19th International Conference on Computational Linguistics · **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/C02-1078/) · [PDF](https://aclanthology.org/C02-1078.pdf) · [arXiv cs/0206030](https://arxiv.org/abs/cs/0206030) · [OpenAlex](https://openalex.org/W2077810047)

**Citations:** 42 citations (openalex-snapshot · 2026-10-14) · [23](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Japanese often omits obligatory arguments of verbs. These "zero pronouns" do not appear in the text, so a system must find them before it can link them to antecedents. Earlier corpus-based work mostly addressed antecedent selection alone and relied on costly annotated data. Seki, Fujii and Ishikawa propose one probabilistic framework that combines both steps. The first factor estimates whether a missing case of a verb is a zero pronoun, using verb–case co-occurrence counts. The second factor scores candidate antecedents. It is split into a syntactic model (particle, sentence distance, relative-clause constraint) trained on a small annotated set, and a semantic model trained on large unannotated newspaper text. A certainty score lets the system output only confident analyses. In evaluation on 30 hand-annotated Kyotodaigaku Text Corpus articles, the combined model selected the correct antecedent first in 50.7% of cases. Adding the detection factor raised accuracy at every coverage level. The approach shows that cheap unannotated text can offset the scarcity of anaphora annotation.

## Key points
- **Pipeline:**
  - JUMAN handles segmentation and POS tagging, and the KNP parser handles syntax.
  - Candidate zero pronouns (ga/wo/ni cases) are filtered with the IPAL verb dictionary, which covers case frames for 911 verbs.
  - Nominative ga is always treated as obligatory.
- **Features:**
  - Six features are used: verb, case, particle, distance, relative-clause flag, and semantic class.
  - Semantic classes come from the 544 classes of the Bunruigoihyou thesaurus, which covers about 55K nouns.
- **Training data:**
  - The semantic model was built from six years of Mainichi Shimbun text, yielding about 12M verb–noun co-occurrences for 6,194 verb types.
  - The evaluation set has 449 zero pronouns and was tested with leave-one-out cross-validation over 30 articles.
- **Learning curves:**
  - Accuracy rose steeply with the first ~5 annotated articles.
  - More unannotated text gave only modest, but supervision-free, gains.
- **Results:**
  - The best combination (Both2) reached 50.7% top-1 accuracy.
  - A rule-based baseline in the results table reached 40.1% at top-1 and 58.6% at top-3.
  - Lowering coverage via the certainty threshold (t = 0.5) increased accuracy.

*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/en/shelves/linguistics/)

Topics: [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/)

## Cited by

Cited by

23 papers of our library cite it (of the 140673 whose bibliographies we read); 14 of them have a page here, 9 not yet.

- [Anaphoric Zero Pronoun Identification: A Multilingual Approach](https://md.research.elamur.ai/free/en/papers/aloraini-2020-anaphoric-zero-pronoun-identification-multilingual/)
- [Cross-lingual Zero Pronoun Resolution](https://md.research.elamur.ai/free/en/papers/aloraini-2020-cross-lingual-zero-pronoun-resolution/)
- [Chinese Zero Pronoun Resolution: An Unsupervised Probabilistic Model Rivaling Supervised Resolvers](https://md.research.elamur.ai/free/en/papers/chen-2014-chinese-zero-pronoun-resolution-unsupervised/)
- [Chinese Zero Pronoun Resolution: A Joint Unsupervised Discourse-Aware Model Rivaling State-of-the-Art Resolvers](https://md.research.elamur.ai/free/en/papers/chen-2015-chinese-zero-pronoun-resolution-joint/)
- [Chinese Zero Pronoun Resolution with Deep Neural Networks](https://md.research.elamur.ai/free/en/papers/chen-2016-chinese-zero-pronoun-resolution-deep/)
- [Exploiting Syntactic Patterns as Clues in Zero-Anaphora Resolution](https://md.research.elamur.ai/free/en/papers/iida-2006-exploiting-syntactic-patterns-as-clues/)
- [A Cross-Lingual ILP Solution to Zero Anaphora Resolution](https://md.research.elamur.ai/free/en/papers/iida-2011-cross-lingual-ilp-solution-zero/)
- [Intra-sentential Zero Anaphora Resolution using Subject Sharing Recognition](https://md.research.elamur.ai/free/en/papers/iida-2015-intra-sentential-zero-anaphora-resolution/)
- [Intra-Sentential Subject Zero Anaphora Resolution using Multi-Column Convolutional Neural Network](https://md.research.elamur.ai/free/en/papers/iida-2016-intra-sentential-subject-zero-anaphora/)
- [Japanese Zero Pronoun Resolution based on Ranking Rules and Machine Learning](https://md.research.elamur.ai/free/en/papers/isozaki-2003-japanese-zero-pronoun-resolution-based/)
- [An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution](https://md.research.elamur.ai/free/en/papers/konno-2020-empirical-study-contextual-data-augmentation/)
- [A Fully-Lexicalized Probabilistic Model for Japanese Zero Anaphora Resolution](https://md.research.elamur.ai/free/en/papers/sasano-2008-fully-lexicalized-probabilistic-model-japanese/)
- [A Discriminative Approach to Japanese Zero Anaphora Resolution with Large-scale Lexicalized Case Frames](https://md.research.elamur.ai/free/en/papers/sasano-2011-discriminative-approach-japanese-zero-anaphora/)
- [Identification and Resolution of Chinese Zero Pronouns: A Machine Learning Approach](https://md.research.elamur.ai/free/en/papers/zhao-2007-identification-resolution-chinese-zero-pronouns/)

## Cite

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

```bibtex
@inproceedings{seki2002probabilistic,
  title = {{A Probabilistic Method for Analyzing Japanese Anaphora Integrating Zero Pronoun Detection and Resolution}},
  author = {Seki, Kazuhiro and Fujii, Atsushi and Ishikawa, Tetsuya},
  booktitle = {COLING 2002: The 19th International Conference on Computational Linguistics},
  year = {2002},
  doi = {10.3115/1072228.1072306},
  eprint = {cs/0206030},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.3115/1072228.1072306}
}
```

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