Lezama-Sánchez, A.L.; Tovar Vidal, M.; Reyes-Ortiz, J.A. An Approach Based on Semantic Relationship Embeddings for Text Classification. Mathematics2022, 10, 4161.
Lezama-Sánchez, A.L.; Tovar Vidal, M.; Reyes-Ortiz, J.A. An Approach Based on Semantic Relationship Embeddings for Text Classification. Mathematics 2022, 10, 4161.
Lezama-Sánchez, A.L.; Tovar Vidal, M.; Reyes-Ortiz, J.A. An Approach Based on Semantic Relationship Embeddings for Text Classification. Mathematics2022, 10, 4161.
Lezama-Sánchez, A.L.; Tovar Vidal, M.; Reyes-Ortiz, J.A. An Approach Based on Semantic Relationship Embeddings for Text Classification. Mathematics 2022, 10, 4161.
Abstract
Embedding representation models characterize each word as a vector of numbers with a
fixed length. These models have been used in tasks involving text classification, such as recommen-
dation and question-answer systems. Semantic relationships are words with a relationship between
them providing a complete idea to a text. Therefore, it is hypothesized that an embedding model
involving semantic relationships will provide better performance for tasks that use them. This paper
presents three embedding models based on semantic relations extracted fromWikipedia to classify
texts. The synonym, hyponym, and hyperonym semantic relationships were the ones considered
in this work since previous experiments have shown that they are the ones that provide the most
semantic knowledge. Lexical-syntactic patterns present in the literature were implemented and
subsequently applied to the Wikipedia corpus to obtain the semantic relationships present in it.
Several semantic relationships are used in different models: synonymy, hyponym-hyperonym, and a
combination of the first two. A convolutional neural network was trained for text classification to
evaluate the performance of each model. The results obtained were evaluated with the metrics of
precision, accuracy, recall, and F1-measure. The best values obtained with the second model were
accuracy of 0.79 for the 20-Newsgroup corpus. F1-measure and recall of 0.87 respectively for the Reuters corpus.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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