The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing
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
Modeling by shortest data description
Rissanen, J. (1978) · Automatica · doi:10.1016/0005-1098(78)90005-5 · Cited by 6,011
Learning Logical Definitions from Relations
Quinlan, J. R. (1990) · Machine Learning · doi:10.1023/a:1022699322624 · Cited by 1,644
Learning to parse database queries using inductive logic programming
Zelle, J. M., & Mooney, R. J. (1996) · Cited by 642
Self-citationDeveloping a natural language interface to complex data
Hendrix, G. G., Sacerdoti, E. D., Sagalowicz, D., & Slocum, J. (1978) · ACM Transactions on Database Systems · doi:10.1145/320251.320253 · Cited by 534
Relational Learning of Pattern-Match Rules for Information Extraction.
Califf, M. E., & Mooney, R. J. (1997) · Cited by 507
Self-citationEstimating probabilities: a crucial task in machine learning
Cestnik, B. (1990) · European Conference on Artificial Intelligence · Cited by 459
Machine Invention of First-order Predicates by Inverting Resolution
MUGGLETON, S., & BUNTINE, W. (1988) · Machine Learning Proceedings 1988 · doi:10.1016/b978-0-934613-64-4.50040-2 · Cited by 436
A fully statistical approach to natural language interfaces
Miller, S., Stallard, D. G., Bobrow, R., & Schwartz, R. M. (1996) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/981863.981871 · Cited by 136
The application of semantic classification trees to natural language understanding
Kühn, R., & Mori, R. D. (1995) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.391397 · Cited by 120
Compression, Significance and Accuracy
Muggleton, S., Srinivasan, A., & Bain, M. (1992) · Elsevier eBooks · doi:10.1016/b978-1-55860-247-2.50048-6 · Cited by 57
Cited by
Yu, T., Zhang, R., Yang, K., Yasunaga, M., Wang, D., Li, Z., Ma, J., Li, I., Yao, Q., Roman, S., et al. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1425 · Cited by 1,034
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
Zhong, V., Xiong, C., & Socher, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.00103 · Cited by 733
Zettlemoyer, L., & Collins, M. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1207.1420 · Cited by 728
Language to Logical Form with Neural Attention
Dong, L., & Lapata, M. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1004 · Cited by 700
A Syntactic Neural Model for General-Purpose Code Generation
Yin, P., & Neubig, G. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1041 · Cited by 623
Metadata
- Authors
- Tang, L. R., & Mooney, R. J.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2001
- Pages
- 466-477
- ISSN
- 0302-9743 (ISSN-L)
- OpenAlex
- W2096979215
- Cited by
- 261
- References
- 13
- FWCI
- 1.34
- Type
- conference paper
- Language
- en
- Open access
- closed
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: Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing. https://app.cochat.ai/papers/10.1007/3-540-44795-4_40
- The paper
- Tang, L. R., & Mooney, R. J. (2001). Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing. Lecture Notes in Computer Science. https://doi.org/10.1007/3-540-44795-4_40
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.
