TY - JOUR
T1 - A learning framework for information block search based on probabilistic graphical models and Fisher Kernel
AU - Wong, Tak Lam
AU - Xie, Haoran
AU - Lam, Wai
AU - Wang, Fu Lee
N1 - Publisher Copyright:
© 2017, Springer-Verlag Berlin Heidelberg.
PY - 2018/9/1
Y1 - 2018/9/1
N2 - Contrary to traditional Web information retrieval methods that can only return a ranked list of Web pages and only allow search terms in the query, we have developed a novel learning framework for retrieving precise information blocks from Web pages given a query, which may contain some search terms and prior information such as the layout format of the data. There are two challenging sub-tasks for this problem. One challenge is information block detection, where a Web page is automatically segmented into blocks. Another challenge is to find the information blocks relevant to the query. Existing page segmentation methods, which make use of only visual layout information or only content information, do not consider the query information, leading to a solution having conflict with the information need expressed by the query. Our framework aims at modeling the query and the block features to capture both keyword information and prior information via a probabilistic graphical model. Fisher Kernel, which can effectively incorporate the graphical model, is then employed to accomplish the two sub-tasks in a unified manner, optimizing the final goal of block retrieval performance. We have conducted experiments on benchmark datasets and read-world data. Comparisons between existing methods have been conducted to evaluate the effectiveness of our framework.
AB - Contrary to traditional Web information retrieval methods that can only return a ranked list of Web pages and only allow search terms in the query, we have developed a novel learning framework for retrieving precise information blocks from Web pages given a query, which may contain some search terms and prior information such as the layout format of the data. There are two challenging sub-tasks for this problem. One challenge is information block detection, where a Web page is automatically segmented into blocks. Another challenge is to find the information blocks relevant to the query. Existing page segmentation methods, which make use of only visual layout information or only content information, do not consider the query information, leading to a solution having conflict with the information need expressed by the query. Our framework aims at modeling the query and the block features to capture both keyword information and prior information via a probabilistic graphical model. Fisher Kernel, which can effectively incorporate the graphical model, is then employed to accomplish the two sub-tasks in a unified manner, optimizing the final goal of block retrieval performance. We have conducted experiments on benchmark datasets and read-world data. Comparisons between existing methods have been conducted to evaluate the effectiveness of our framework.
KW - Fisher Kernel
KW - Graphical models
KW - Information block retrieval
KW - Information extraction
UR - http://www.scopus.com/inward/record.url?scp=85051241841&partnerID=8YFLogxK
U2 - 10.1007/s13042-017-0657-9
DO - 10.1007/s13042-017-0657-9
M3 - Article
AN - SCOPUS:85051241841
SN - 1868-8071
VL - 9
SP - 1473
EP - 1487
JO - International Journal of Machine Learning and Cybernetics
JF - International Journal of Machine Learning and Cybernetics
IS - 9
ER -