--- title: "TextRunner: Open Information Extraction on the Web (2007)" description: "This short demonstration paper presents TextRunner, a working system for Open Information… 5 references, cited by 73 papers here." authors: ["Alexander Yates", "Michele Banko", "Matthew Broadhead", "Michael Cafarella", "Oren Etzioni", "Stephen Soderland"] year: 2007 venue: "Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT)" source: "https://aclanthology.org/N07-4013/" 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/yates-2007-textrunner-open-information-extraction-web/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/yates-2007-textrunner-open-information-extraction-web/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2007](https://md.research.elamur.ai/free/en/papers/2007/) / TextRunner: Open Information Extraction on the Web # TextRunner: Open Information Extraction on the Web **Authors:** [Alexander Yates](https://md.research.elamur.ai/free/en/authors/alexander-yates/), [Michele Banko](https://md.research.elamur.ai/free/en/authors/michele-banko/), Matthew Broadhead, [Michael Cafarella](https://md.research.elamur.ai/free/en/authors/michael-cafarella/), [Oren Etzioni](https://md.research.elamur.ai/free/en/authors/oren-etzioni/) and [Stephen Soderland](https://md.research.elamur.ai/free/en/authors/stephen-soderland/) · **Year:** 2007 · **Venue:** [Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT)](https://md.research.elamur.ai/free/en/venues/naacl/) ([NAACL 2007](https://md.research.elamur.ai/free/en/venues/naacl/2007/)) · **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/N07-4013/) · [PDF](https://aclanthology.org/N07-4013.pdf) **Citations:** [73](https://md.research.elamur.ai/free/en/papers/yates-2007-textrunner-open-information-extraction-web/cited-by/) in our library, of the 149,439 papers whose bibliographies we read ## Summary This short demonstration paper presents TextRunner, a working system for Open Information Extraction (OIE). Classic extraction systems target a fixed list of relations and depend on labelled examples or hand-written patterns. TextRunner instead reads a whole corpus once, with no human input, and collects every relational triple it can find. It chunks sentences into noun phrases and checks nearby pairs of noun phrases with a lightweight classifier. That classifier is trained on examples labelled automatically by running a full parser and applying heuristics to a small sample. A separate component, Resolver, groups strings that refer to the same entity or relation, and an inverted index spread over a cluster supports fast interactive queries. The public demo covers 117 million web pages. On a 9-million-document test set, the system produced 7.8 million well-formed tuples, about 80% of which human judges accepted as correct. It also made fewer errors than the relation-specific KnowItAll system. The work shows that relation-independent extraction can run at Web scale. ## Key points - Extractions are triples of the form (entity, relation, entity), for example (Edison, invented, light bulbs). - On a 9M-document subset (133 million sentences), extraction used under 68 CPU hours, or about 4 hours on the cluster. - The test corpus yielded 7.8M well-formed tuples, and 80.4% of a random sample of 400 were judged correct. - Against KnowItAll on ten frequent relations, both systems found similar numbers of correct extractions (11,631 vs. 11,476). TextRunner's error rate was 12% versus 18%, a one-third reduction. - Resolver applies an unsupervised probabilistic model to decide whether two strings co-refer, based on the tuples they appear in. ## Interesting ideas - **Expensive tool as teacher, cheap model at runtime.** A full parser labels training data offline, and a fast POS-based classifier then does the extraction at scale. This pattern could serve any low-resource language where a good parser exists but is too slow for large corpora. - **Heuristic self-labelling instead of manual annotation.** Rule-based checks on parsed sentences produce positive and negative examples automatically. This avoids annotation costs and makes it easy to adapt the system to a new domain or language. - **Single-pass design as a hard constraint.** Limiting processing to one linear pass, unlike clustering methods that scale quadratically with document count, keeps the system feasible on Web-sized data. This is a useful design principle for large crawls. - **Distributional synonym merging.** Two strings are treated as likely synonyms when they appear with similar argument tuples. This turns noisy, open-ended relation phrases into usable clusters without a predefined schema. - **Indexing extractions for querying.** Storing the tuples in an inverted index, with optional expansion to synonyms, makes the extracted knowledge immediately browsable. This also helps with quick manual quality checks. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* 📚 Shelves (2): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works 🧭 Topics (1): [🔎 Information extraction](https://md.research.elamur.ai/free/en/topics/information-extraction/) · 167 works ## Cite Cite as: Alexander Yates, Michele Banko, Matthew Broadhead, Michael Cafarella, Oren Etzioni and Stephen Soderland. 2007. TextRunner: Open Information Extraction on the Web. Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT). The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{yates2007textrunner, title = {{TextRunner: Open Information Extraction on the Web}}, author = {Alexander Yates, Michele Banko, Matthew Broadhead, Michael Cafarella, Oren Etzioni and Stephen Soderland}, booktitle = {Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT)}, year = {2007}, url = {https://aclanthology.org/N07-4013/} } ``` - [← previous LinES: An English-Swedish Parallel Treebank](https://md.research.elamur.ai/free/en/papers/ahrenberg-2007-lines-english-swedish-parallel-treebank/) - [next → Semantic Class Induction and Coreference Resolution](https://md.research.elamur.ai/free/en/papers/ng-2007-semantic-class-induction-coreference-resolution/) - [↑ back to papers of 2007](https://md.research.elamur.ai/free/en/papers/2007/) ## References 5 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. M. Banko, M. J. Cafarella, S. Soderland, M. Broadhead, and O. Etzioni. 2007. [Open Information Extraction from the Web](https://md.research.elamur.ai/free/en/papers/banko-2007-open-information-extraction-web/). In IJCAI. 2. A. Culotta, A. McCallum, and J. Betz. 2006. Integrating Probabilistic Extraction Models and Relational Data Mining to Discover Relations and Patterns in Text. In HLT-NAACL. [find](https://scholar.google.com/scholar?q=A%2E+Culotta%2C+A%2E+McCallum%2C+and+J%2E+Betz%2E+2006%2E+Integrating+Probabilistic+Extraction+Models+and+Relational+Data+Mining+to+Discover+Relations+and+Patterns+in+Text%2E+In+HLT%2DNAACL%2E) 3. O. Etzioni, M. Cafarella, D. Downey, S. Kok, A. Popescu, T. Shaked, S. Soderland, D. Weld, and A. Yates. 2005. Unsupervised Named-Entity Extraction from the Web: An Experimental Study. Artificial Intelligence, 165(1):91– 134. [find](https://scholar.google.com/scholar?q=O%2E+Etzioni%2C+M%2E+Cafarella%2C+D%2E+Downey%2C+S%2E+Kok%2C+A%2E+Popescu%2C+T%2E+Shaked%2C+S%2E+Soderland%2C+D%2E+Weld%2C+and+A%2E+Yates%2E+2005%2E+Unsupervised+Named%2DEntity+Extraction+from+the+Web%3A+An+Experimental+Study%2E+Artificial+Inte) 4. Y. Shinyama and S. Sekine. 2006. [Preemptive Information Extraction Using Unrestricted Relation Discovery](https://md.research.elamur.ai/free/en/papers/shinyama-2006-preemptive-information-extraction-unrestricted-relation/). In HLT-NAACL. 5. A. Yates and O. Etzioni. 2007. Unsupervised Resolution of Objects and Relations on the Web. In NAACL-HLT. [find](https://scholar.google.com/scholar?q=A%2E+Yates+and+O%2E+Etzioni%2E+2007%2E+Unsupervised+Resolution+of+Objects+and+Relations+on+the+Web%2E+In+NAACL%2DHLT%2E) *Updated 29 October 2026*