Jointly learning word embeddings using a corpus and a knowledge base

• Published in: PLOS ONE Vol. 13; no. 3; p. e0193094
• Main Authors: Alsuhaibani, Mohammed, Bollegala, Danushka, Maehara, Takanori, Kawarabayashi, Ken-ichi
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science (PLoS) 12.03.2018; Public Library of Science
• Subjects: Algorithms; Analysis; Biology and Life Sciences; Cats; Computer and Information Sciences; Computer science; Data Mining; Data Mining - methods; Distance learning; Humans; Knowledge base; Knowledge Bases; Knowledge bases (artificial intelligence); Knowledge representation; Learning; Lexicology; Linguistics; Medicine; Methods; Natural Language Processing; Objective function; Physical Sciences; Q; R; Research and Analysis Methods; Research Article; Science; Semantics; Social Sciences; Texts; Vector spaces; United Kingdom > UK; Japan

Methods for representing the meaning of words in vector spaces purely using the information distributed in text corpora have proved to be very valuable in various text mining and natural language processing (NLP) tasks. However, these methods still disregard the valuable semantic relational structur...