---
title: "Automatic Acquisition of Domain Knowledge for Information"
description: "The authors propose ExDisco, an unsupervised bootstrapping procedure that needs only a handful of… 11 references, cited by 62 papers here."
authors: ["Roman Yangarber", "Ralph Grishman", "Pasi Tapanainen", "Silja Huttunen"]
year: 2000
venue: "COLING 2000 Volume 2: The 18th International Conference on Computational Linguistics"
source: "https://aclanthology.org/C00-2136/"
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/yangarber-2000-automatic-acquisition-domain-knowledge-information/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/yangarber-2000-automatic-acquisition-domain-knowledge-information/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2000](https://md.research.elamur.ai/free/en/papers/2000/) / Automatic Acquisition of Domain Knowledge for Information Extraction

# Automatic Acquisition of Domain Knowledge for Information Extraction

**Authors:** [Roman Yangarber](https://md.research.elamur.ai/free/en/authors/roman-yangarber/), [Ralph Grishman](https://md.research.elamur.ai/free/en/authors/ralph-grishman/), [Pasi Tapanainen](https://md.research.elamur.ai/free/en/authors/pasi-tapanainen/) and Silja Huttunen · **Year:** 2000 · **Venue:** [COLING 2000 Volume 2: The 18th International Conference on Computational Linguistics](https://md.research.elamur.ai/free/en/venues/coling/) ([COLING 2000](https://md.research.elamur.ai/free/en/venues/coling/2000/)) · **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/C00-2136/) · [PDF](https://aclanthology.org/C00-2136.pdf) · [OpenAlex](https://openalex.org/W1971563386)

**Citations:** 234 citations (OpenAlex, 2026-10-14) · [62](https://md.research.elamur.ai/free/en/papers/yangarber-2000-automatic-acquisition-domain-knowledge-information/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Adapting an Information Extraction system to a new event scenario requires finding the many ways the target events are phrased. That search normally means reading or annotating large amounts of text. The authors propose ExDisco, an unsupervised bootstrapping procedure that needs only a handful of user-supplied seed patterns and a raw, unlabelled domain corpus.

The corpus is parsed with a Conexor FDG dependency parser. Names, numbers and dates are normalized, and each clause is turned into a subject–verb–object tuple. ExDisco then alternates between two steps:
- splitting documents into relevant and non-relevant sets using the current patterns;
- promoting the candidate pattern whose occurrences are most concentrated in the relevant set.

Document relevance is graded rather than binary, so evidence from later, less trusted patterns counts for less.

On the MUC-6 Management Succession scenario, the Proteus extractor with discovered (lightly reviewed) patterns outperformed a version tuned by hand for a month and came close to the heavily developed current system. Document-filtering quality fell within the range of supervised or hand-built MUC-6 systems. This shows that much costly corpus analysis can be automated.

## Key points
- Training data was 9,224 Wall Street Journal articles. Parsing produced about 440,000 clause tuples, of which roughly 215,000 were distinct.
- Seeds were tiny:
  - **Management Succession:** company + appoint-type verb + person, and person + resign-type verb.
  - **Mergers/Acquisitions:** buy/purchase + company, and company + merge.
- Candidate patterns are scored by their precision on relevant documents multiplied by the log of their relevant-document count. Overly frequent and very rare patterns are filtered out before scoring.
- Text filtering was evaluated over 100 iterations against the MUC-6 training and test sets (100 documents each) and against 200 hand-judged Acquisition documents. Recall/precision was comparable to MUC-6 participants.
- Unlike Riloff (1996), ExDisco learns complete multi-slot event patterns and requires no hand-classified relevant documents.

## Interesting ideas
- **Learn patterns and relevant documents together.** Each is used to refine the other, which breaks the circularity of needing relevant text to find patterns and patterns to find relevant text. This mutual-bootstrapping setup suits any low-resource task where labelled examples are scarce, such as Ukrainian-language domain extraction.
- **Graded document relevance.** Seed-matched documents count fully, while documents reached through newly learned patterns receive inherited, lower confidence. This damps semantic drift during bootstrapping and is easy to add to other self-training loops.
- **Break sparse tuples into argument pairs.** Rare full tuples are scored as pairs (e.g., verb–object or subject–object), and the missing slot is filled afterwards with high-scoring words. This yields reliable statistics and, as a side effect, word classes (e.g., verbs linking company and person).
- **Normalize before mining.** Replacing names, numbers and dates with class tokens, and collapsing passives and relative clauses into one form, makes surface variants countable as a single pattern. It is a cheap step that strengthens almost any corpus pattern-mining effort.
- **Use the by-product as a free metric.** The procedure's document relevance ranking can be scored directly as a text-filtering task. This gives a way to monitor a bootstrapping run without building full extraction gold data.

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

## Institutions

- [New York University](https://md.research.elamur.ai/free/en/universities/new-york-university/) — University, New York, United States · 136 works · 86,390 citations
- [University of Helsinki](https://md.research.elamur.ai/free/en/universities/university-of-helsinki/) — University, Helsinki, Finland · 33 works · 4,705 citations

Also on the byline: [Connexor](https://ror.org/04wmwts90).

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,086 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,889 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 9,296 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 9,206 works

🧭 Topics (1): [🔎 Information extraction](https://md.research.elamur.ai/free/en/topics/information-extraction/) · 109 works

## Cite

Cite as: Roman Yangarber, Ralph Grishman, Pasi Tapanainen and Silja Huttunen. 2000. Automatic Acquisition of Domain Knowledge for Information Extraction. COLING 2000 Volume 2: The 18th International Conference on Computational Linguistics.

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

```bibtex
@inproceedings{yangarber2000automatic,
  title = {{Automatic Acquisition of Domain Knowledge for Information Extraction}},
  author = {Roman Yangarber, Ralph Grishman, Pasi Tapanainen and Silja Huttunen},
  booktitle = {COLING 2000 Volume 2: The 18th International Conference on Computational Linguistics},
  year = {2000},
  doi = {10.3115/992730.992782},
  url = {https://doi.org/10.3115/992730.992782}
}
```

- [← previous A Comparison of Alignment Models for Statistical Machine Translation](https://md.research.elamur.ai/free/en/papers/och-2000-comparison-alignment-models-statistical-machine/)
- [next → The rhetorical parsing of unrestricted texts: a surface-based approach](https://md.research.elamur.ai/free/en/papers/marcu-2000-rhetorical-parsing-unrestricted-texts-surface/)
- [↑ back to papers of 2000](https://md.research.elamur.ai/free/en/papers/2000/)

## References

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

1. David Fisher, Stephen Soderland, Joseph Mc-Carthy, Fangfang Feng, and Wendy Lehnert. 1995. Description of the UMass system as used for MUC-6. In Proc. Sixth Message Understanding Conf. (MUC-6) , Columbia, MD, November. Morgan Kaufmann. [find](https://scholar.google.com/scholar?q=David+Fisher%2C+Stephen+Soderland%2C+Joseph+Mc%2DCarthy%2C+Fangfang+Feng%2C+and+Wendy+Lehnert%2E+1995%2E+Description+of+the+UMass+system+as+used+for+MUC%2D6%2E+In+Proc%2E+Sixth+Message+Understanding+Conf%2E+%28MUC%2D6%29+%2C+Colum)
2. Ralph Grishman. 1995. The NYU system for MUC-6, or where's the syntax? In Proc. Sixth Message Understanding Conf. (MUC-6) , pages 167{176, Columbia, MD, November. Morgan Kaufmann. [find](https://scholar.google.com/scholar?q=Ralph+Grishman%2E+1995%2E+The+NYU+system+for+MUC%2D6%2C+or+where%27s+the+syntax%3F+In+Proc%2E+Sixth+Message+Understanding+Conf%2E+%28MUC%2D6%29+%2C+pages+167%7B176%2C+Columbia%2C+MD%2C+November%2E+Morgan+Kaufmann%2E)
3. W. Lehnert, C. Cardie, D. Fisher, J. McCarthy, E. Rilo , and S. Soderland. 1992. University of massachusetts: MUC-4 test results and analysis. In Proc. Fourth Message Understanding Conf. , McLean, VA, June. Morgan Kaufmann. [find](https://scholar.google.com/scholar?q=W%2E+Lehnert%2C+C%2E+Cardie%2C+D%2E+Fisher%2C+J%2E+McCarthy%2C+E%2E+Rilo+%2C+and+S%2E+Soderland%2E+1992%2E+University+of+massachusetts%3A+MUC%2D4+test+results+and+analysis%2E+In+Proc%2E+Fourth+Message+Understanding+Conf%2E+%2C+McLean%2C+VA%2C)
4. Scott Miller, Michael Crystal, Heidi Fox, Lance Ramshaw, Richard Schwartz, Rebecca Stone, Ralph Weischedel, and the Annotation Group. 1998. Algorithms that learn to extract information; BBN: Description of the SIFT system as used for MUC-7. In Proc. 7th Message Understanding Conf. , Fairfax, VA. 199… [find](https://scholar.google.com/scholar?q=Scott+Miller%2C+Michael+Crystal%2C+Heidi+Fox%2C+Lance+Ramshaw%2C+Richard+Schwartz%2C+Rebecca+Stone%2C+Ralph+Weischedel%2C+and+the+Annotation+Group%2E+1998%2E+Algorithms+that+learn+to+extract+information%3B+BBN%3A+Descripti)
5. Morgan Kaufmann. 1995. Proceedings of the Sixth Message Understanding Conference (MUC-6) , Columbia, MD, November. Morgan Kaufmann. [find](https://scholar.google.com/scholar?q=Morgan+Kaufmann%2E+1995%2E+Proceedings+of+the+Sixth+Message+Understanding+Conference+%28MUC%2D6%29+%2C+Columbia%2C+MD%2C+November%2E+Morgan+Kaufmann%2E)
6. Ellen Rilo and Rosie Jones. 1999. Learning dictionaries for information extraction by multi-level bootstrapping. In Proc. 16th Nat'l Conference on Arti cial Intelligence (AAAI-99) , Orlando, Florida. [find](https://scholar.google.com/scholar?q=Ellen+Rilo+and+Rosie+Jones%2E+1999%2E+Learning+dictionaries+for+information+extraction+by+multi%2Dlevel+bootstrapping%2E+In+Proc%2E+16th+Nat%27l+Conference+on+Arti+cial+Intelligence+%28AAAI%2D99%29+%2C+Orlando%2C+Florida%2E)
7. Ellen Rilo . 1996. Automatically generating extraction patterns from untagged text. In Proc. 13th Nat'l Conf. on Arti cial Intelligence (AAAI-96) . The AAAI Press/MIT Press. [find](https://scholar.google.com/scholar?q=Ellen+Rilo+%2E+1996%2E+Automatically+generating+extraction+patterns+from+untagged+text%2E+In+Proc%2E+13th+Nat%27l+Conf%2E+on+Arti+cial+Intelligence+%28AAAI%2D96%29+%2E+The+AAAI+Press%2FMIT+Press%2E)
8. Pasi Tapanainen and Timo Jarvinen. 1997. [A non-projective dependency parser](https://md.research.elamur.ai/free/en/papers/tapanainen-1997-non-projective-dependency-parser/). In Proc. 5th Conf. on Applied Natural Language Processing , pages 64{71, Washington, D.C. ACL.
9. Roman Yangarber and Ralph Grishman. 1997. Customization of information extraction systems. In Paola Velardi, editor, Int'l Workshop on Lexically Driven Information Extraction , Frascati, Italy. Universita di Roma. [find](https://scholar.google.com/scholar?q=Roman+Yangarber+and+Ralph+Grishman%2E+1997%2E+Customization+of+information+extraction+systems%2E+In+Paola+Velardi%2C+editor%2C+Int%27l+Workshop+on+Lexically+Driven+Information+Extraction+%2C+Frascati%2C+Italy%2E+Univer)
10. Roman Yangarber and Ralph Grishman. 1998. NYU: Description of the Proteus/PET system as used for MUC-7 ST. In 7th Message Understanding Conference , Columbia, MD. [find](https://scholar.google.com/scholar?q=Roman+Yangarber+and+Ralph+Grishman%2E+1998%2E+NYU%3A+Description+of+the+Proteus%2FPET+system+as+used+for+MUC%2D7+ST%2E+In+7th+Message+Understanding+Conference+%2C+Columbia%2C+MD%2E)
11. Roman Yangarber, Ralph Grishman, Pasi Tapanainen, and Silja Huttunen. 2000. Unsupervised discovery of scenario-level patterns for information extraction. In Proc. Conf. on Applied Natural Language Processing (ANLP-NAACL) , Seattle, WA. [find](https://scholar.google.com/scholar?q=Roman+Yangarber%2C+Ralph+Grishman%2C+Pasi+Tapanainen%2C+and+Silja+Huttunen%2E+2000%2E+Unsupervised+discovery+of+scenario%2Dlevel+patterns+for+information+extraction%2E+In+Proc%2E+Conf%2E+on+Applied+Natural+Language+Pro)

*Updated 24 October 2026*