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Aligning coordinated text streams from multiple sources and multiplelanguages has opened many new research venues on cross-lingual knowledgediscovery. In this paper we aim to advance state-of-the-art by: (1). extendingcoarse-grained topic-level knowledge mining to fine-grained information unitssuch as entities and events; (2). following a novelData-to-Network-to-Knowledge (D2N2K) paradigm to construct and utilize networkstructures to capture and propagate reliable evidence. We introduce a novelBurst Information Network (BINet) representation that can display the mostimportant information and illustrate the connections among bursty entities,events and keywords in the corpus. We propose an effective approach toconstruct and decipher BINets, incorporating novel criteria based onmulti-dimensional clues from pronunciation, translation, burst, neighbor andgraph topological structure. The experimental results on Chinese and Englishcoordinated text streams show that our approach can accurately decipher thenodes with high confidence in the BINets and that the algorithm can beefficiently run in parallel, which makes it possible to apply it to hugeamounts of streaming data for never-ending language and informationdecipherment.

Aligning Coordinated Text Streams through Burst Information Network Construction and Decipherment