TY - JOUR
T1 - Thread Structure Learning on Online Health Forums with Partially Labeled Data
AU - Liu, Yunzhong
AU - Shi, Jinhe
AU - Chen, Yi
N1 - Funding Information:
Manuscript received February 8, 2019; revised August 23, 2019; accepted September 26, 2019. Date of publication October 29, 2019; date of current version December 9, 2019. This work was supported in part by the Leir Foundation and in part by the National Institutes of Health under Grant UL1TR003017. (Yunzhong Liu and Jinhe Shi contributed equally to this work.) (Corresponding author: Yi Chen.) Y. Liu is with the Department of Computer Science, Arizona State University, Tempe, AZ 85281 USA (e-mail: liuyz@asu.edu).
Publisher Copyright:
© 2014 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Thread structures, the reply relationships between posts, in online forums are very important for readers to understand the thread content, and for improving the effectiveness of automated forum information retrieval, expert findings, and so on. However, most online forums only have partially labeled structures, which means that some reply relationships are known while the others are unknown. To address this problem, studies have been performed to learn and predict thread structures. However, existing work does not leverage the partially available thread structures to learn the complete thread structure. We have also observed that many online health forums are a type of person-centric forums, where persons are mentioned across posts, providing hints about the reply relationships between posts. In this article, we first proposed to learn the complete thread structures by leveraging the partially known structures based on a statistical machine learning model - thread conditional random fields (threadCRFs). Then, we proposed to use person resolution, the process of identifying the same person mentioned in different contexts, together with threadCRF for thread structure learning. We have empirically verified the effectiveness of the proposed approaches.
AB - Thread structures, the reply relationships between posts, in online forums are very important for readers to understand the thread content, and for improving the effectiveness of automated forum information retrieval, expert findings, and so on. However, most online forums only have partially labeled structures, which means that some reply relationships are known while the others are unknown. To address this problem, studies have been performed to learn and predict thread structures. However, existing work does not leverage the partially available thread structures to learn the complete thread structure. We have also observed that many online health forums are a type of person-centric forums, where persons are mentioned across posts, providing hints about the reply relationships between posts. In this article, we first proposed to learn the complete thread structures by leveraging the partially known structures based on a statistical machine learning model - thread conditional random fields (threadCRFs). Then, we proposed to use person resolution, the process of identifying the same person mentioned in different contexts, together with threadCRF for thread structure learning. We have empirically verified the effectiveness of the proposed approaches.
KW - Thread conditional random fields (threadCRF)
KW - thread structure learning
UR - http://www.scopus.com/inward/record.url?scp=85076642762&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076642762&partnerID=8YFLogxK
U2 - 10.1109/TCSS.2019.2946498
DO - 10.1109/TCSS.2019.2946498
M3 - Article
AN - SCOPUS:85076642762
SN - 2329-924X
VL - 6
SP - 1273
EP - 1282
JO - IEEE Transactions on Computational Social Systems
JF - IEEE Transactions on Computational Social Systems
IS - 6
M1 - 8886425
ER -