Aligning Coordinated Text Streams through Burst Information Network Construction and Decipherment

TL;DR

This paper introduces a novel Burst Information Network (BINet) framework for aligning and deciphering coordinated multilingual text streams, capturing fine-grained entities and events to enhance cross-lingual knowledge discovery.

Contribution

It extends knowledge mining from topics to entities and events using a new network-based paradigm and introduces an effective method for constructing and deciphering BINets.

Findings

High-confidence node decipherment in BINets

Efficient parallel algorithm for large-scale data

Accurate cross-lingual information alignment

Abstract

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.