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Activity Number: 301 - Natural Language Processing Applications in Defense and National Security
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 11:50 AM
Sponsor: Section on Statistics in Defense and National Security
Abstract #313726
Title: Few-Shot Learning for Text Applications: Exploring Authorship Identification with Small Data
Author(s): Lauren Phillips* and Sarah Reehl and Ana Usenko
Companies: Pacific Northwest National Laboratory and Pacific Northwest National Laboratory and Western Washington University
Keywords: Few-shot learning; Authorship identification; Natural language processing; Deep learning

The goal of few-shot learning is to generalize a classifier’s performance to new classes given relatively small amounts of data. It has remained a difficult challenge for machine learning algorithms and even more so when it is merged with natural language processing. Authorship identification, in particular, is a challenging problem that generally relies on large quantities of data. In this talk we introduce a dataset for few-shot authorship identification and compare results from state-of-the-art deep learning models and traditional stylometric approaches.

Authors who are presenting talks have a * after their name.

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