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Automatic Detection and Classification of Social Events
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BibTeX
@MISC{Agarwal_automaticdetection,
author = {Apoorv Agarwal and Owen Rambow},
title = {Automatic Detection and Classification of Social Events},
year = {}
}
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Abstract
In this paper we introduce the new task of social event extraction from text. We distinguish two broad types of social events depending on whether only one or both parties are aware of the social contact. We annotate part of Automatic Content Extraction (ACE) data, and perform experiments using Support Vector Machines with Kernel methods. We use a combination of structures derived from phrase structure trees and dependency trees. A characteristic of our events (which distinguishes them from ACE events) is that the participating entities can be spread far across the parse trees. We use syntactic and semantic insights to devise a new structure derived from dependency trees and show that this plays a role in achieving the best performing system for both social event detection and classification tasks. We also use three data sampling approaches to solve the problem of data skewness. Sampling methods improve the F1-measure for the task of relation detection by over 20 % absolute over the baseline. 1
Keyphrases
social event automatic detection dependency tree semantic insight social event extraction new task broad type performing system new structure kernel method automatic content extraction support vector machine ace event parse tree social event detection phrase structure tree social contact classification task data skewness relation detection
