The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Semantic taxonomy induction from heterogenous evidence
Open accessAbstract
We propose a novel algorithm for inducing semantic taxonomies. Previous algorithms for taxonomy induction have typically focused on independent classifiers for discovering new single relationships based on hand-constructed or automatically discovered textual patterns. By contrast, our algorithm flexibly incorporates evidence from multiple classifiers over heterogenous relationships to optimize the entire structure of the taxonomy, using knowledge of a word's coordinate terms to help in determining its hypernyms, and vice versa. We apply our algorithm on the problem of sense-disambiguated noun hyponym acquisition, where we combine the predictions of hypernym and coordinate term classifiers with the knowledge in a preexisting semantic taxonomy (WordNet 2.1). We add 10,000 novel synsets to WordNet 2.1 at 84% precision, a relative error reduction of 70% over a non-joint algorithm using the same component classifiers. Finally, we show that a taxonomy built using our algorithm shows a 23% relative F-score improvement over WordNet 2.1 on an independent testset of hypernym pairs.
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
WordNet: An Electronic Lexical Database
Kilgarriff, A., & Fellbaum, C. (2000) · Language · doi:10.2307/417141 · Cited by 11,464
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
CYC: a large-scale investment in knowledge infrastructure
Lenat, D. B. (1995) · Communications of the ACM · doi:10.1145/219717.219745 · Cited by 1,946
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
Learning Syntactic Patterns for Automatic Hypernym Discovery
Snow, R., Jurafsky, D. S., & Ng, A. Y. (2004) · neural information processing systems · Cited by 658
Self-citationDependency-Based Evaluation of Minipar
Lin, D. (2003) · Text, Speech and Language Technology · doi:10.1007/978-94-010-0201-1_18 · Cited by 595
Noun classification from predicate-argument structures
Hindle, D. (1990) · Proceedings of the 28th annual meeting on Association for Computational Linguistics - · doi:10.3115/981823.981857 · Cited by 545
Ontology Learning from Text: Methods, Evaluation and Applications
Magnini, B., Paul Buitelaar, C., & Cimiano, P. (2005) · Cited by 429
VerbOcean: Mining the Web for Fine-Grained Semantic Verb Relations
Chklovski, T. A., & Pantel, P. (2004) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · Cited by 391
Automatic construction of a hypernym-labeled noun hierarchy from text
Caraballo, S. A. (1999) · Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics - · doi:10.3115/1034678.1034705 · Cited by 373
Cited by
Yago: a core of semantic knowledge
Suchanek, F. M., Kasneci, G., & Weikum, G. (2007) · Proceedings of the 16th international conference on World Wide Web · doi:10.1145/1242572.1242667 · Cited by 2,512
Bordes, A., Glorot, X., Weston, J., & Bengio, Y. (2014) · Machine Learning · doi:10.1007/s10994-013-5363-6 · Cited by 675
Kilgarriff, A. (2007) · Computational Linguistics · doi:10.1162/coli.2007.33.1.147 · Cited by 286
(2014) · Sussex Research Online (University of Sussex) · Cited by 163
HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment
Vulić, I., Gerz, D., Kiela, D., Hill, F., & Korhonen, A. (2016) · arXiv · doi:10.48550/arxiv.1608.02117 · Cited by 90
Metadata
- Authors
- Snow, R., Jurafsky, D., & Ng, A. Y.
- Venue
- Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06
- Published
- 2006
- Pages
- 801-808
- OpenAlex
- W2144108169
- Cited by
- 441
- References
- 16
- FWCI
- 25.21
- 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: Semantic taxonomy induction from heterogenous evidence. https://app.cochat.ai/papers/10.3115/1220175.1220276
- The paper
- Snow, R., Jurafsky, D., & Ng, A. Y. (2006). Semantic taxonomy induction from heterogenous evidence. Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06. https://doi.org/10.3115/1220175.1220276
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.
