Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the context of Ephemeral Clustering of b Pages, it can be interesting to label each cluster with a small summary instead of just a label. Within this scope,  introduce the paradigm of Textual Entailment by Generality, which can be defined as the entailment from a specific b snippet towards a more general b snippet. The subjacent idea is to find the best b snippet, which summarizes and subsumes all the other b snippets within an ephemeral cluster. To reach this objective,  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 b 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.
['Gaël Dias', 'Sebastião Pais', 'Katarzyna Wegrzyn-Wolska', 'Robert Mahl']
I am writing a research paper on a idea Recognizing Textual Entailment by Generality Using Informative Asymmetric Measures and Multiword Unit Identification to Summarize Ephemeral Clusters
[{"from": "human", "value": "I am writing a research paper on a idea Recognizing Textual Entailment by Generality Using Informative Asymmetric Measures and Multiword Unit Identification to Summarize Ephemeral Clusters"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the context of Ephemeral Clustering of b Pages, it can be interesting to label each cluster with a small summary instead of just a label. Within this scope,  introduce the paradigm of Textual Entailment by Generality, which can be defined as the entailment from a specific b snippet towards a more general b snippet. The subjacent idea is to find the best b snippet, which summarizes and subsumes all the other b snippets within an ephemeral cluster. To reach this objective,  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 b 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."}]