  Aligning coordinated text streams from multiple sources and multiple
languages has opened many new research venues on cross-lingual knowledge
discovery. In this paper we aim to advance state-of-the-art by: (1). extending
coarse-grained topic-level knowledge mining to fine-grained information units
such as entities and events; (2). following a novel
Data-to-Network-to-Knowledge (D2N2K) paradigm to construct and utilize network
structures to capture and propagate reliable evidence. We introduce a novel
Burst Information Network (BINet) representation that can display the most
important information and illustrate the connections among bursty entities,
events and keywords in the corpus. We propose an effective approach to
construct and decipher BINets, incorporating novel criteria based on
multi-dimensional clues from pronunciation, translation, burst, neighbor and
graph topological structure. The experimental results on Chinese and English
coordinated text streams show that our approach can accurately decipher the
nodes with high confidence in the BINets and that the algorithm can be
efficiently run in parallel, which makes it possible to apply it to huge
amounts of streaming data for never-ending language and information
decipherment.
