Abstract
In this paper, we investigate the interactions between topic persons to help readers construct the background knowledge of a topic. We proposed a rich interactive tree structure to represent syntactic, context, and semantic information of text, and this structure is incorporated into a treebased convolution kernel to identify segments that convey person interactions and further construct person interaction networks. Empirical evaluations demonstrate that the proposed method is effective in detecting and extracting the interactions between topic persons in the text, and outperforms other extraction approaches used for comparison. Furthermore, readers will be able to easily navigate through the topic persons of interest within the interaction networks, and further construct the background knowledge of the topic to facilitate comprehension.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017 |
| Publisher | IEEE Computer Society |
| Pages | 13-14 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781509065431 |
| DOIs | |
| Publication status | Published - May 16 2017 |
| Externally published | Yes |
| Event | 33rd IEEE International Conference on Data Engineering, ICDE 2017 - San Diego, United States Duration: Apr 19 2017 → Apr 22 2017 |
Conference
| Conference | 33rd IEEE International Conference on Data Engineering, ICDE 2017 |
|---|---|
| Country | United States |
| City | San Diego |
| Period | 4/19/17 → 4/22/17 |
Fingerprint
ASJC Scopus subject areas
- Software
- Signal Processing
- Information Systems
Cite this
SPIRIT : A tree kernel-based method for topic person interaction detection (Extended abstract). / Chang, Yung Chun; Chen, Chien Chin; Hsu, Wen Lian.
Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017. IEEE Computer Society, 2017. p. 13-14 7929909.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - SPIRIT
T2 - A tree kernel-based method for topic person interaction detection (Extended abstract)
AU - Chang, Yung Chun
AU - Chen, Chien Chin
AU - Hsu, Wen Lian
PY - 2017/5/16
Y1 - 2017/5/16
N2 - In this paper, we investigate the interactions between topic persons to help readers construct the background knowledge of a topic. We proposed a rich interactive tree structure to represent syntactic, context, and semantic information of text, and this structure is incorporated into a treebased convolution kernel to identify segments that convey person interactions and further construct person interaction networks. Empirical evaluations demonstrate that the proposed method is effective in detecting and extracting the interactions between topic persons in the text, and outperforms other extraction approaches used for comparison. Furthermore, readers will be able to easily navigate through the topic persons of interest within the interaction networks, and further construct the background knowledge of the topic to facilitate comprehension.
AB - In this paper, we investigate the interactions between topic persons to help readers construct the background knowledge of a topic. We proposed a rich interactive tree structure to represent syntactic, context, and semantic information of text, and this structure is incorporated into a treebased convolution kernel to identify segments that convey person interactions and further construct person interaction networks. Empirical evaluations demonstrate that the proposed method is effective in detecting and extracting the interactions between topic persons in the text, and outperforms other extraction approaches used for comparison. Furthermore, readers will be able to easily navigate through the topic persons of interest within the interaction networks, and further construct the background knowledge of the topic to facilitate comprehension.
UR - http://www.scopus.com/inward/record.url?scp=85021249385&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85021249385&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2017.13
DO - 10.1109/ICDE.2017.13
M3 - Conference contribution
AN - SCOPUS:85021249385
SP - 13
EP - 14
BT - Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017
PB - IEEE Computer Society
ER -