Sneha Mehta, Mohammad Raihanul Islam, Naren Ramakrishnan


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


Naren Ramakrishnan

Sneha Mehta

Publication Details

Date of publication:
May 13, 2019
World Wide Web conference
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