---
title: "Preemptive Information Extraction using Unrestricted Relation"
description: "Shinyama and Sekine propose Preemptive Information Extraction, which builds every plausible IE… 10 references, cited by 76 papers here."
authors: ["Yusuke Shinyama", "Satoshi Sekine"]
year: 2006
venue: "Proceedings of the Human Language Technology Conference of the NAACL, Main Conference"
source: "https://aclanthology.org/N06-1039/"
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/shinyama-2006-preemptive-information-extraction-unrestricted-relation/"
---
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# Preemptive Information Extraction using Unrestricted Relation Discovery

**Authors:** Yusuke Shinyama and [Satoshi Sekine](https://md.research.elamur.ai/free/en/authors/satoshi-sekine/) · **Year:** 2006 · **Venue:** [Proceedings of the Human Language Technology Conference of the NAACL, Main Conference](https://md.research.elamur.ai/free/en/venues/naacl/) ([NAACL 2006](https://md.research.elamur.ai/free/en/venues/naacl/2006/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-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/N06-1039/) · [PDF](https://aclanthology.org/N06-1039.pdf) · [OpenAlex](https://openalex.org/W2012179495)

**Citations:** 236 citations (OpenAlex, 2026-10-14) · [76](https://md.research.elamur.ai/free/en/papers/shinyama-2006-preemptive-information-extraction-unrestricted-relation/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Traditional Information Extraction (IE) needs heavy manual work for every new scenario: someone must choose the target event type, collect relevant documents and build extraction patterns, without knowing whether the task is feasible. Shinyama and Sekine propose Preemptive Information Extraction, which builds every plausible IE table ahead of time so that adapting IE becomes a matter of searching existing tables. The core method, Unrestricted Relation Discovery, works as follows:
- News is crawled from several sources and grouped into "basic clusters" of articles about the same event.
- Entities are linked across those articles with cross-document coreference.
- Each entity is described by "basic patterns" taken from GLARF predicate-argument structures.
- Basic clusters are grouped into "metaclusters" (tables) when several entity pairs share weighted patterns and the texts are similar enough in vocabulary.

From about 28k articles the system produced 101 tables with at least three rows. Of 48 sampled tables, 75% were judged consistent. This early, fully unsupervised result suggests that relation tables can be discovered without any scenario specification.

## Key points
- Data scale: 28,009 source articles formed 5,543 basic clusters. These yielded 643,767 basic pattern tokens (7,990 types), 302 metaclusters, and 101 metaclusters with three or more rows.
- Evaluation: 36 of 48 randomly chosen tables (75%) were consistent, and 118 of 161 rows (73%) matched the evaluator's description.
- Recall estimate: the largest table, on hurricanes, was compared with a hand-built list and recovered 6 of 9 storms. Hurricane names were mostly separated from person names, even though the NE tagger did not distinguish them.
- Error analysis of 10 wrong rows:
  - 4 came from coreference mistakes.
  - 1 came from a parse error.
  - 4 came from distant patterns that belonged to different events within the same cluster.
  - 1 had an unclear cause.
- Basic patterns come from GLARF (Meyers et al.). GLARF regularizes constructions such as participials and coordination, and it converts easily into a word-level directed graph.

## Interesting ideas
- **Treat IE adaptation as retrieval.** Building candidate relation tables in advance turns the costly task of developing an extractor into simply looking up an existing table. This could suit any domain where requirements arrive unpredictably.
- **Use the pattern as the column.** Syntactic context around an entity serves both as a clustering feature and as a label for the entity's role. Entities from different documents therefore line up into the same column without any predefined schema.
- **Pool comparable articles to fight sparsity.** Merging several reports of the same event, and linking their entities with coreference, gives each entity far more context than a single article would. This trick is useful in low-resource settings, such as smaller-language news.
- **Add a topical gate that ignores names.** Requiring bag-of-words similarity between documents, with names excluded, keeps unrelated events with shared surface patterns apart (e.g., a politician vs. a storm "flying to" a place). Down-weighting very frequent patterns such as "say" adds further protection against spurious matches.
- **Evaluate open-ended output by describability.** A judge writes one specific templated sentence ($1, $2) for each table and checks how many rows fit it. This gives a cheap, interpretable consistency measure when no gold schema exists.

*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

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

## Cite

Cite as: Yusuke Shinyama and Satoshi Sekine. 2006. Preemptive Information Extraction using Unrestricted Relation Discovery. Proceedings of the Human Language Technology Conference of the NAACL, Main Conference.

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

```bibtex
@inproceedings{shinyama2006preemptive,
  title = {{Preemptive Information Extraction using Unrestricted Relation Discovery}},
  author = {Yusuke Shinyama and Satoshi Sekine},
  booktitle = {Proceedings of the Human Language Technology Conference of the NAACL, Main Conference},
  year = {2006},
  doi = {10.3115/1220835.1220874},
  url = {https://doi.org/10.3115/1220835.1220874}
}
```

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## References

10 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Eugene Agichtein and L. Gravano. 2000. Snowball: Extracting Relations from Large Plaintext Collections. In Proceedings of the 5th ACM International Conference on Digital Libraries (DL-00). [find](https://scholar.google.com/scholar?q=Eugene+Agichtein+and+L%2E+Gravano%2E+2000%2E+Snowball%3A+Extracting+Relations+from+Large+Plaintext+Collections%2E+In+Proceedings+of+the+5th+ACM+International+Conference+on+Digital+Libraries+%28DL%2D00%29%2E)
2. Sergey Brin. 1998. Extracting Patterns and Relations from the World Wide Web. In WebDB Workshop at EDBT ’98. [find](https://scholar.google.com/scholar?q=Sergey+Brin%2E+1998%2E+Extracting+Patterns+and+Relations+from+the+World+Wide+Web%2E+In+WebDB+Workshop+at+EDBT+%E2%80%9998%2E)
3. Eugene Charniak. 2000. [A maximum-entropy-inspired parser](https://md.research.elamur.ai/free/en/papers/charniak-2000-maximum-entropy-inspired-parser/). In Proceedings of NAACL-2000.
4. Takaaki Hasegawa, Satoshi Sekine, and Ralph Grishman. 2004. [Discovering relations among named entities from large corpora](https://md.research.elamur.ai/free/en/papers/hasegawa-2004-discovering-relations-among-named-entities/). In Proceedings of the Annual Meeting of Association of Computational Linguistics (ACL-04).
5. Adam Meyers, Ralph Grishman, Michiko Kosaka, and Shubin Zhao. 2001a. Covering Treebanks with GLARF. In ACL/EACL Workshop on Sharing Tools and Resources for Research and Education. [find](https://scholar.google.com/scholar?q=Adam+Meyers%2C+Ralph+Grishman%2C+Michiko+Kosaka%2C+and+Shubin+Zhao%2E+2001a%2E+Covering+Treebanks+with+GLARF%2E+In+ACL%2FEACL+Workshop+on+Sharing+Tools+and+Resources+for+Research+and+Education%2E)
6. Adam Meyers, Michiko Kosaka, Satoshi Sekine, Ralph Grishman, and Shubin Zhao. 2001b. Parsing and GLARFing. In Proceedings of RANLP-2001, Tzigov Chark, Bulgaria. [find](https://scholar.google.com/scholar?q=Adam+Meyers%2C+Michiko+Kosaka%2C+Satoshi+Sekine%2C+Ralph+Grishman%2C+and+Shubin+Zhao%2E+2001b%2E+Parsing+and+GLARFing%2E+In+Proceedings+of+RANLP%2D2001%2C+Tzigov+Chark%2C+Bulgaria%2E)
7. Deepak Ravichandran and Eduard Hovy. 2002. [Learning surface text patterns for a question answering system](https://md.research.elamur.ai/free/en/papers/ravichandran-2002-learning-surface-text-patterns-question/). In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL).
8. Ellen Riloff. 1996. Automatically Generating Extraction Patterns from Untagged Text. In Proceedings of the 13th National Conference on Artificial Intelligence (AAAI-96). [find](https://scholar.google.com/scholar?q=Ellen+Riloff%2E+1996%2E+Automatically+Generating+Extraction+Patterns+from+Untagged+Text%2E+In+Proceedings+of+the+13th+National+Conference+on+Artificial+Intelligence+%28AAAI%2D96%29%2E)
9. Kiyoshi Sudo, Satoshi Sekine, and Ralph Grishman. 2003. An Improved Extraction Pattern Representation Model for Automatic IE Pattern Acquisition. In Proceedings of the Annual Meeting of Association of Computational Linguistics (ACL-03). [find](https://scholar.google.com/scholar?q=Kiyoshi+Sudo%2C+Satoshi+Sekine%2C+and+Ralph+Grishman%2E+2003%2E+An+Improved+Extraction+Pattern+Representation+Model+for+Automatic+IE+Pattern+Acquisition%2E+In+Proceedings+of+the+Annual+Meeting+of+Association+of)
10. Roman Yangarber, Ralph Grishman, Pasi Tapanainen, and Silja Huttunen. 2000. Unsupervised Discovery of Scenario-Level Patterns for Information Extraction. In Proceedings of the 18th International Conference on Computational Linguistics (COLING-00). [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+Proceedings+of+the+18th+International+Confe)

*Updated 24 October 2026*