Mining News Events from Comparable News Corpora: A Multi-Attribute Proximity Network Modeling Approach

TL;DR

ProxiModel is a new framework for extracting structured news events from large, noisy news data by modeling event correlations within and across documents using a proximity-network, enabling scalable and interpretable event mining.

Contribution

It introduces the proximity-network data structure and a probabilistic modeling approach for scalable, high-quality news event extraction from large corpora.

Findings

Effective in generating high-quality event descriptors and attributes

Robust across different news corpora

Applicable to news summarization and event tracking

Abstract

We present ProxiModel, a novel event mining framework for extracting high-quality structured event knowledge from large, redundant, and noisy news data sources. The proposed model differentiates itself from other approaches by modeling both the event correlation within each individual document as well as across the corpus. To facilitate this, we introduce the concept of a proximity-network, a novel space-efficient data structure to facilitate scalable event mining. This proximity network captures the corpus-level co-occurence statistics for candidate event descriptors, event attributes, as well as their connections. We probabilistically model the proximity network as a generative process with sparsity-inducing regularization. This allows us to efficiently and effectively extract high-quality and interpretable news events. Experiments on three different news corpora demonstrate that the proposed method is effective and robust at generating high-quality event descriptors and attributes. We briefly detail many interesting applications from our proposed framework such as news summarization, event tracking and multi-dimensional analysis on news. Finally, we explore a case study on visualizing the events for a Japan Tsunami news corpus and demonstrate ProxiModel's ability to automatically summarize emerging news events.