The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Distant supervision for relation extraction without labeled data
Open accessAbstract
Modern models of relation extraction for tasks like ACE are based on supervised learning of relations from small hand-labeled corpora. We investigate an alternative paradigm that does not require labeled corpora, avoiding the domain dependence of ACE-style algorithms, and allowing the use of corpora of any size. Our experiments use Freebase, a large semantic database of several thousand relations, to provide distant supervision. For each pair of entities that appears in some Freebase relation, we find all sentences containing those entities in a large unlabeled corpus and extract textual features to train a relation classifier. Our algorithm combines the advantages of supervised IE (combining 400,000 noisy pattern features in a probabilistic classifier) and unsupervised IE (extracting large numbers of relations from large corpora of any domain). Our model is able to extract 10,000 instances of 102 relations at a precision of 67.6%. We also analyze feature performance, showing that syntactic parse features are particularly helpful for relations that are ambiguous or lexically distant in their expression.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Freebase: a collaboratively created graph database for structuring human knowledge
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., & Taylor, J. (2008) · Proceedings of the 2008 ACM SIGMOD international conference on Management of data · doi:10.1145/1376616.1376746 · Cited by 4,852
Automatic acquisition of hyponyms from large text corpora
Hearst, M. A. (1992) · Proceedings of the 14th conference on Computational linguistics - · doi:10.3115/992133.992154 · Cited by 3,253
Incorporating non-local information into information extraction systems by Gibbs sampling
Finkel, J. R., Grenager, T., & Manning, C. (2005) · Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05 · doi:10.3115/1219840.1219885 · Cited by 3,008
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
Snow, R., O'Connor, B., Jurafsky, D., & Ng, A. Y. (2008) · Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08 · doi:10.3115/1613715.1613751 · Cited by 1,887
Self-citationOpen information extraction from the web
Banko, M., Cafarella, M., Soderland, S. G., Broadhead, M., & Etzioni, O. (2007) · Cited by 1,410
Snowball: extracting relations from large plain-text collections
Agichtein, E., & Gravano, L. (2000) · Proceedings of the fifth ACM conference on Digital libraries · doi:10.1145/336597.336644 · Cited by 1,147
Unsupervised named-entity extraction from the Web: An experimental study
Etzioni, O., Cafarella, M., Downey, D., Popescu, A.-M., Shaked, T., Soderland, S., Weld, D. S., & Yates, A. (2005) · Artificial Intelligence · doi:10.1016/j.artint.2005.03.001 · Cited by 1,124
The Automatic Content Extraction (ACE) Program – Tasks, Data, and Evaluation
Doddington, G. R., Mitchell, A., Przybocki, M. A., Ramshaw, L., Strassel, S. M., & Weischedel, R. (2004) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/4o6ysc5sicba · Cited by 1,029
Open information extraction from the web
Etzioni, O., Banko, M., Soderland, S., & Weld, D. S. (2008) · Communications of the ACM · doi:10.1145/1409360.1409378 · Cited by 1,012
Extracting Patterns and Relations from the World Wide Web
Brin, S. (1999) · Lecture Notes in Computer Science · doi:10.1007/10704656_11 · Cited by 999
Cited by
(2009) · Minds at UW (University of Wisconsin) · Cited by 4,791
Knowledge Graph Embedding by Translating on Hyperplanes
Wang, Z., Zhang, J., Feng, J., & Chen, Z. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8870 · Cited by 3,915
Learning Entity and Relation Embeddings for Knowledge Graph Completion
Lin, Y., Liu, Z., Sun, M., Liu, Y., & Zhu, X. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9491 · Cited by 3,749
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3070843 · Cited by 2,947
Knowledge Graph Embedding: A Survey of Approaches and Applications
Wang, Q., Mao, Z., Wang, B., & Guo, L. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2754499 · Cited by 2,654
Metadata
- Authors
- Mintz, M., Bills, S., Snow, R., & Jurafsky, D.
- Venue
- Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - ACL-IJCNLP '09
- Published
- 2009
- Volume
- 2
- Pages
- 1003
- OpenAlex
- W2107598941
- Cited by
- 2,752
- References
- 25
- FWCI
- 30.08
- Type
- conference paper
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Distant supervision for relation extraction without labeled data. https://app.cochat.ai/papers/10.3115/1690219.1690287
- The paper
- Mintz, M., Bills, S., Snow, R., & Jurafsky, D. (2009). Distant supervision for relation extraction without labeled data. Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - ACL-IJCNLP '09. https://doi.org/10.3115/1690219.1690287
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
