We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic pro...
Mark Steyvers, Padhraic Smyth, Michal Rosen-Zvi, T...
Most traditional text clustering methods are based on "bag of words" (BOW) representation based on frequency statistics in a set of documents. BOW, however, ignores the ...
Jian Hu, Lujun Fang, Yang Cao, Hua-Jun Zeng, Hua L...
We describe cross language retrieval experiments using Amharic queries and English language document collection from our participation in the bilingual ad hoc track at the CLEF 20...
This paper presents a novel opinion mining research problem, which is called Contrastive Opinion Modeling (COM). Given any query topic and a set of text collections from multiple ...
We present a query-driven algorithm for the distributed indexing of large document collections within structured P2P networks. To cope with bandwidth consumption that has been ide...
Gleb Skobeltsyn, Toan Luu, Ivana Podnar Zarko, Mar...