O'Keefe, Simon Edward Marius orcid.org/0000-0001-5957-2474 and Alrashdi, Reem Mansour M. (2018) Deep Learning and Word Embeddings for Tweet Classification for Crisis Response. In: The 3rd National Computing Colleges Conference, 08-09 Oct 2018.
Abstract
Tradition tweet classification models for crisis response focus on convolutional layers and domain-specific word embeddings. In this paper, we study the application of different neural networks with general-purpose and domain-specific word embeddings to investigate their ability to improve the performance of tweet classification models. We evaluate four tweet classification models on CrisisNLP dataset and obtain comparable results which indicates that general-purpose word embedding such as GloVe can be used instead of domain-specific word embedding especially with Bi-LSTM where results reported the highest performance of 62.04% F1 score.
Metadata
Item Type: | Conference or Workshop Item |
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Authors/Creators: |
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Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 27 Mar 2019 11:30 |
Last Modified: | 13 Mar 2025 05:37 |
Status: | Published |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:144141 |
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Filename: Deep_Learning_and_Word_Embeddings_for_Tweet_Classification_for_Crisis_Response_.pdf
Description: Deep Learning and Word Embeddings for Tweet Classification for Crisis Response