Name
Title | ||
|---|---|---|
Automatic Extraction and Quantitative Evaluation of the Character Relationship Networks from Children’s Literature works |
Abstract | ||
|---|---|---|
To automate the graded reading task, we urgently need to extract and calculate the important index of the complexity of the relationship between the characters affecting the plot complexity of narrative literature. In order to realize this purpose, this paper describes a computational method for automatic analysis of the virtual social network from children's literature works. We selected the required bibliography for primary school students recommended by the Ministry of Education, then automatically extract the characters of the novel by CRF, and constructs the character network based on the co-occurrence relationship. The statistical analysis method of complex network provides a quantitative basis for distinguishing the complexity of characters' relationships in different texts. The results show that the structural characteristics of character interaction networks are similar to those of small world networks, and the selected network measurement indexes are significantly related to the complexity of text characters. Finally, we achieved effectively evaluating and predicting the complexity of the social networks from more extensive literature works some classical regression model based on machine learning. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IALP48816.2019.9037669 | 2019 International Conference on Asian Language Processing (IALP) |
Keywords | DocType | ISSN |
Graded reading,Automatic analysis of text,Children’s Literature works,machine learning,complex network | Conference | 2159-1962 |
ISBN | Citations | PageRank |
978-1-7281-5015-4 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kun Ma | 1 | 0 | 0.34 |
| Lijiao Yang | 2 | 0 | 1.69 |