Recognising textual entailment with logical inference
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Combining association measures for collocation extraction
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Topic segmentation algorithms for text summarization and passage retrieval: an exhaustive evaluation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
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In the context of Ephemeral Clustering of web Pages, it can be interesting to label each cluster with a small summary instead of just a label. Within this scope, we introduce the paradigm of Textual Entailment by Generality, which can be defined as the entailment from a specific web snippet towards a more general web snippet. The subjacent idea is to find the best web snippet, which summarizes and subsumes all the other web snippets within an ephemeral cluster. To reach this objective, we first propose a new informative asymmetric similarity measure called the Simplified Asymmetric InfoSimba(AISs), which can be combined with different asymmetric association measures. In particular, the AISs proposes an unsupervised language-independent solution to infer Textual Entailment by Generality and as such can help to encounter the web snippet with maximum semantic coverage. This new methodology is tested against the first Recognizing Textual Entailment data set (RTE-1)1 for an exhaustive number of asymmetric association measures with and without the identification of Multiword Units. The comparative experiments with existing state-of-the-art methodologies show promising results.