TY - GEN
T1 - Kider
T2 - 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020
AU - Chen, Yu Ting
AU - Lin, Zheng Wen
AU - Chang, Yung Chun
AU - Hsu, Wen Lian
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Advancement of deep learning has improved performances on a wide variety of tasks. However, language reasoning and understanding remain difficult tasks in Natural Language Processing (NLP). In this work, we consider this problem and propose a novel Knowledge-Infused Document Embedding Representation (KIDER) for text categorization. We use knowledge patterns to generate high quality document representation. These patterns preserve categorical-distinctive semantic information, provide interpretability, and achieve superior performances at the same time. Experiments show that the KIDER model outperforms state-of-the-art methods on two important NLP tasks, i.e., emotion analysis and news topic detection, by 7% and 20%. In addition, we also demonstrate the potential of highlighting important information for each category and news using these patterns. These results show the value of knowledge-infused patterns in terms of interpretability and performance enhancement.
AB - Advancement of deep learning has improved performances on a wide variety of tasks. However, language reasoning and understanding remain difficult tasks in Natural Language Processing (NLP). In this work, we consider this problem and propose a novel Knowledge-Infused Document Embedding Representation (KIDER) for text categorization. We use knowledge patterns to generate high quality document representation. These patterns preserve categorical-distinctive semantic information, provide interpretability, and achieve superior performances at the same time. Experiments show that the KIDER model outperforms state-of-the-art methods on two important NLP tasks, i.e., emotion analysis and news topic detection, by 7% and 20%. In addition, we also demonstrate the potential of highlighting important information for each category and news using these patterns. These results show the value of knowledge-infused patterns in terms of interpretability and performance enhancement.
KW - Knowledge representation
KW - Natural Language Processing
KW - Text categorization
UR - http://www.scopus.com/inward/record.url?scp=85091274515&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85091274515&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-55789-8_2
DO - 10.1007/978-3-030-55789-8_2
M3 - Conference contribution
AN - SCOPUS:85091274515
SN - 9783030557881
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 18
EP - 29
BT - Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices - 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020, Proceedings
A2 - Fujita, Hamido
A2 - Sasaki, Jun
A2 - Fournier-Viger, Philippe
A2 - Ali, Moonis
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 22 September 2020 through 25 September 2020
ER -