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Cited by 541

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

DIRT @SBT@discovery of inference rules from text

Lin, Dekang, Pantel, Patrick·2001·Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
DOI 10.1145/502512.502559
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Abstract

In this paper, we propose an unsupervised method for discovering inference rules from text, such as is author of Y a X wrote Y, solved Y a X found a solution to Y, and caused Y a Y is triggered by X. Inference rules are extremely important in many fields such as natural language processing, information retrieval, and artificial intelligence in general. Our algorithm is based on an extended version of Harris' Distributional Hypothesis, which states that words that occurred in the same contexts tend to be similar. Instead of using this hypothesis on words, we apply it to paths in the dependency trees of a parsed corpus.

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Metadata

Authors
Lin, D., & Pantel, P.
Venue
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Publisher
ACM
Published
2001-08-26
Pages
323-328
DOI
10.1145/502512.502559
OpenAlex
W1965605789
Cited by
541
References
1
FWCI
4.02
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: DIRT @SBT@discovery of inference rules from text. https://app.cochat.ai/papers/10.1145/502512.502559
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Lin, D., & Pantel, P. (2001). DIRT @SBT@discovery of inference rules from text. Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/502512.502559

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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.