Sequence-Based Word Embeddings for Effective Text Classification

Autor:	Bruno Guilherme Gomes, Fabricio Murai, Olga Goussevskaia, Ana Paula Couto da Silva
Rok vydání:	2021
Předmět:	Sequence Word embedding Markov chain business.industry Computer science media_common.quotation_subject Sampling (statistics) Machine learning computer.software_genre Scalability Embedding Quality (business) Artificial intelligence business computer Word (computer architecture) media_common
Zdroj:	Natural Language Processing and Information Systems ISBN: 9783030805982 NLDB
DOI:	10.1007/978-3-030-80599-9_12
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::4c649e67a23041f9156d9112732036ac https://doi.org/10.1007/978-3-030-80599-9_12 Zobrazit plný text záznamu