• Corpus ID: 12141896

When logical inference helps determining textual entailment ( and when it doesn ’ t )

@inproceedings{BosWhenLI,
  title={When logical inference helps determining textual entailment ( and when it doesn ’ t )},
  author={Johan Bos and Katja Markert}
}
We compare and combine two methods to approach the second textual entailment challenge (RTE-2): a shallow method based mainly on word-overlap and a method based on logical inference, using first-order theorem proving and model building techniques. We use a machine learning technique to combine features of both methods. We submitted two runs, one using only the shallow features, yielding an accuracy of 61.6%, and one using features of both methods, performing with an accuracy score of 60.6… 
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86 Citations
Highly Influential Citations
4
Background Citations
38
Methods Citations
25
Results Citations
1

Tables from this paper

86 Citations

Semantic and Logical Inference Model for Textual Entailment
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An approach to textual entailment recognition is presented, in which inference is based on a shallow semantic representation of relations in the text and hypothesis of the entailment pair, and in which specialized knowledge is encapsulated in modular components with very simple interfaces.
Natural Logic for Textual Inference
TLDR
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Recognizing Textual Entailment with Deep-Shallow Semantic Analysis and Logical Inference
TLDR
The architecture and evaluation of a new system for recognizing textual entailment (RTE) is presented, conceived as an adaptable and modular system allowing for a high-coverage syntactic and semantic text analysis combined with logical inference.
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TLDR
This special issue of the JNLE provides an opportunity to showcase some of the most important work in this emerging area of textual entailment, particularly automatic acquisition of paraphrases and lexical semantic relationships and unsupervised inference in applications such as question answering, information extraction and summarization.
Combining Theorem Proving with Natural Language Processing
TLDR
The LogAnswer system has integrated an automated theorem prover in a framework of natural language processing tools to allow for deductive reasoning over an extensive knowledge base derived from textual sources.
Component-Based Textual Entailment: a Modular and Linguistically-Motivated Framework for Semantic Inferences
TLDR
A framework for component-based TE, where each component is in itself a complete TE system, able to address a TE task on a specific phenomenon in isolation, is proposed.
Automatic Theorem Proving for Natural Logic: a Case Study on Textual Entailment
TLDR
This paper presents an Automatic Theorem Prover for Natural Logic that allows to know precisely the relationships needed in order to reach the entailment in a class of natural language expressions.
A Logic-based Approach for Recognizing Textual Entailment Supported by Ontological Background Knowledge
TLDR
The architecture and the evaluation of a new system for recognizing textual entailment (RTE) are presented, allowing for a high-coverage syntactic and semantic text analysis combined with logical inference and showing that fine-grained and consistent knowledge coming from diverse sources is a necessary condition determining the correctness and traceability of results.
Design and realization of a modular architecture for textual entailment
TLDR
The novel EXCITEMENT architecture, which was developed to enable and encourage the consolidation of methods and resources in the textual entailment area, decomposes RTE into components with strongly typed interfaces, and is available as open source software under the GNU General Public License.
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