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2021 | OriginalPaper | Chapter

Sequence-Based Word Embeddings for Effective Text Classification

Authors : Bruno Guilherme Gomes, Fabricio Murai, Olga Goussevskaia, Ana Paula Couto da Silva

Published in: Natural Language Processing and Information Systems

Publisher: Springer International Publishing

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Abstract

In this work we present DiVe (Distance-based Vector Embedding), a new word embedding technique based on the Logistic Markov Embedding (LME). First, we generalize LME to consider different distance metrics and address existing scalability issues using negative sampling, thus making DiVe scalable for large datasets. In order to evaluate the quality of word embeddings produced by DiVe, we used them to train standard machine learning classifiers, with the goal of performing different Natural Language Processing (NLP) tasks. Our experiments demonstrated that DiVe is able to outperform existing (more complex) machine learning approaches, while preserving simplicity and scalability.

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previous chapter The Importance of Character-Level Information in an Event Detection Model

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http://scikit-learn.org/stable/index.html.

https://allennlp.org/elmo.

https://github.com/google-research/bert.

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Title: Sequence-Based Word Embeddings for Effective Text Classification
Authors: Bruno Guilherme Gomes
Fabricio Murai
Olga Goussevskaia
Ana Paula Couto da Silva
Publisher: Springer International Publishing
Book: Natural Language Processing and Information Systems
Print ISBN: 978-3-030-80598-2

Electronic ISBN: 978-3-030-80599-9

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-80599-9_12

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