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Agglomerative Information Bottleneck
A novel distributional clustering algorithm that maximizes the mutual information per cluster between data and given categories and achieves compression by 3 orders of magnitudes loosing only 10% of the original mutual information.
Document clustering using word clusters via the information bottleneck method
A novel implementation of the recently introduced information bottleneck method for unsupervised document clustering that first finds word-clusters that capture most of the mutual information about to set of documents, and then finds document clusters that preserve the information about the word clusters.
Unsupervised document classification using sequential information maximization
A novel sequential clustering algorithm which is motivated by the Information Bottleneck method is presented, and it is found to be consistently superior to all the other clustering methods examined, typically by a significant margin.
The Information Bottleneck : Theory and Applications
- N. Slonim
- Computer Science
This thesis introduces the first comprehensive review of the Information Bottleneck method along with its recent extension, the multivariate IB, and formulates a well defined info rmation-theoretic framework for unsupervised clustering problems, which is the main focus of this thesis.
A universal framework for regulatory element discovery across all genomes and data types.
Glucose regulates transcription in yeast through a network of signaling pathways
- S. Zaman, Soyeon I Lippman, L. Schneper, N. Slonim, J. Broach
- BiologyMolecular Systems Biology
- 17 February 2009
Activating PKA completely recapitulates the transcriptional growth program in the absence of any increase in growth or metabolism, demonstrating that activation of the growth program results solely from the cell's perception of its nutritional status.
Context Dependent Claim Detection
- Ran Levy, Yonatan Bilu, Daniel Hershcovich, E. Aharoni, N. Slonim
- Computer ScienceInternational Conference on Computational…
- 1 August 2014
This work formally defines the challenging task of automatic claim detection in a given context and outlines a preliminary solution, and assess its performance over annotated real world data, collected specifically for that purpose over hundreds of Wikipedia articles.
Show Me Your Evidence - an Automatic Method for Context Dependent Evidence Detection
- Ruty Rinott, Lena Dankin, Carlos Alzate Perez, Mitesh M. Khapra, E. Aharoni, N. Slonim
- Computer ScienceConference on Empirical Methods in Natural…
- 1 September 2015
This work proposes the task of automatically detecting evidences from unstructured text that support a given claim and suggests a system architecture based on supervised learning to address the evidence detection task.
Multivariate Information Bottleneck
A general principled framework for multivariate extensions of the information bottleneck method is introduced that provides insights about bottleneck variations and enables us to characterize the solutions of these variations.
Stance Classification of Context-Dependent Claims
- Roy Bar-Haim, Indrajit Bhattacharya, Francesco Dinuzzo, Amrita Saha, N. Slonim
- Computer ScienceEACL
- 1 April 2017
This work introduces the complementary task of Claim Stance Classification, along with the first benchmark dataset for this task, and describes an implementation of the model, focusing on a novel algorithm for contrast detection.