TY - GEN
T1 - Chinese Short Text Classification Based on Dependency Syntax Information
AU - Zhang, Yinggang
AU - Xu, Hongguang
AU - Xu, Ke
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/2/2
Y1 - 2021/2/2
N2 - Short text has the characteristics of sparse features and discrete semantics. In order to extract the features of short text better, we propose a short text classification algorithm based on dependency syntax information in this paper. In terms of text representation, we train word vector based on sentences dependency triples. By concatenating the dependency word vector and original word vector, text can be represented at both semantic and syntactic levels. In terms of classification model, we use the dependency syntax information of the short text to guide the state update process of the recurrent neural network. In addition, we run experiments based on Chinese news-title dataset. Experiment results show that the proposed algorithm improves the performance of short text classification remarkably.
AB - Short text has the characteristics of sparse features and discrete semantics. In order to extract the features of short text better, we propose a short text classification algorithm based on dependency syntax information in this paper. In terms of text representation, we train word vector based on sentences dependency triples. By concatenating the dependency word vector and original word vector, text can be represented at both semantic and syntactic levels. In terms of classification model, we use the dependency syntax information of the short text to guide the state update process of the recurrent neural network. In addition, we run experiments based on Chinese news-title dataset. Experiment results show that the proposed algorithm improves the performance of short text classification remarkably.
KW - dependency syntax
KW - long short-term memory network
KW - short text classification
UR - https://www.scopus.com/pages/publications/85112440596
U2 - 10.1145/3456529.3456552
DO - 10.1145/3456529.3456552
M3 - 会议稿件
AN - SCOPUS:85112440596
T3 - ACM International Conference Proceeding Series
SP - 133
EP - 138
BT - ICCDA 2021 - Proceedings of the 2021 5th International Conference on Compute and Data Analysis
PB - Association for Computing Machinery
T2 - 5th International Conference on Compute and Data Analysis, ICCDA 2021
Y2 - 2 February 2021 through 4 February 2021
ER -