DeepAI AI Chat
Log In Sign Up

Utilizing coarse-grained data in low-data settings for event extraction

05/11/2022
by   Osman Mutlu, et al.
0

Annotating text data for event information extraction systems is hard, expensive, and error-prone. We investigate the feasibility of integrating coarse-grained data (document or sentence labels), which is far more feasible to obtain, instead of annotating more documents. We utilize a multi-task model with two auxiliary tasks, document and sentence binary classification, in addition to the main task of token classification. We perform a series of experiments with varying data regimes for the aforementioned integration. Results show that while introducing extra coarse-grained data offers greater improvement and robustness, a gain is still possible with only the addition of negative documents that have no information on any event.

READ FULL TEXT

page 30

page 31

page 32

page 35

page 36

09/06/2022

Few-Shot Document-Level Event Argument Extraction

Event argument extraction (EAE) has been well studied at the sentence le...
08/16/2021

An Effective System for Multi-format Information Extraction

The multi-format information extraction task in the 2021 Language and In...
09/15/2020

Event Presence Prediction Helps Trigger Detection Across Languages

The task of event detection and classification is central to most inform...
06/16/2020

Weakly-supervised Domain Adaption for Aspect Extraction via Multi-level Interaction Transfer

Fine-grained aspect extraction is an essential sub-task in aspect based ...
05/25/2020

AutoMSC: Automatic Assignment of Mathematics Subject Classification Labels

Authors of research papers in the fields of mathematics, and other math-...
08/22/2021

Efficient Algorithms for Learning from Coarse Labels

For many learning problems one may not have access to fine grained label...