Event Detection using Hierarchical Multi-Aspect Attention

Sneha Mehta, Mohammad Raihanul Islam, Naren Ramakrishnan

Abstract

Classical event encoding and extraction methods rely on fixed dictionaries of keywords and templates or require ground truth labels for phrase/sentences. This hinders widespread application of information encoding approaches to large-scale free form (unstructured) text available on the web. Event encoding can be viewed as a hierarchical task where the coarser level task is event detection, i.e., identification of documents containing a specific event, and where the fine-grained task is one of event encoding, i.e., identifying key phrases, key sentences. Hierarchical models with attention seem like a natural choice for this problem, given their ability to differentially attend to more or less important features when constructing document representations. In this work we present a novel factorized bilinear multi-aspect attention mechanism (FBMA) that attends to different aspects of text while constructing its representation. We find that our approach outperforms state-of-the-art baselines for detecting civil unrest, military action, and non-state actor events from corpora in two different languages.

Sneha Mehta, Mohammad Raihanul Islam, Huzefa Rangwala, Naren Ramakrishnan: Event Detection using Hierarchical Multi-Aspect Attention. WWW 2019: 3079-3085