Collective Classification Using Heterogeneous Classifiers

2011 ◽  
pp. 155-169 ◽  
Author(s):  
Zehra Cataltepe ◽  
Abdullah Sonmez ◽  
Kadriye Baglioglu ◽  
Ayse Erzan
Keyword(s):  
Collective Classification

Semi-supervised Collective Classification in Multi-attribute Network Data

2016 ◽  
Vol 45 (1) ◽  
pp. 153-172 ◽  
Author(s):  
Shaokai Wang ◽  
Yunming Ye ◽  
Xutao Li ◽  
Xiaohui Huang ◽  
Raymond Y. K. Lau
Keyword(s):  
Network Data ◽  
Collective Classification

scholarly journals Collective classification method based on label propagation for fraud detection

2020 ◽  
Vol 37 (5) ◽  
pp. 482-489
Author(s):  
Pengya ZHAO ◽  
Xiangling FU ◽  
Weiqiang WU ◽  
Da LI ◽  
Songfeng GAO
Keyword(s):  
Fraud Detection ◽  
Label Propagation ◽  
Classification Method ◽  
Collective Classification

scholarly journals Unrestricted Bridging Resolution

2018 ◽  
Vol 44 (2) ◽  
pp. 237-284 ◽  
Author(s):  
Yufang Hou ◽  
Katja Markert ◽  
Michael Strube
Keyword(s):  
High Reliability ◽  
Class Imbalance ◽  
Collective Classification ◽  
Fine Grained ◽  
Joint Inference ◽  
Second Stage ◽  
Information Status ◽  
Novel Method ◽  
Definition Of ◽  
Marginal Reliability

In contrast to identity anaphors, which indicate coreference between a noun phrase and its antecedent, bridging anaphors link to their antecedent(s) via lexico-semantic, frame, or encyclopedic relations. Bridging resolution involves recognizing bridging anaphors and finding links to antecedents. In contrast to most prior work, we tackle both problems. Our work also follows a more wide-ranging definition of bridging than most previous work and does not impose any restrictions on the type of bridging anaphora or relations between anaphor and antecedent. We create a corpus (ISNotes) annotated for information status (IS), bridging being one of the IS subcategories. The annotations reach high reliability for all categories and marginal reliability for the bridging subcategory. We use a two-stage statistical global inference method for bridging resolution. Given all mentions in a document, the first stage, bridging anaphora recognition, recognizes bridging anaphors as a subtask of learning fine-grained IS. We use a cascading collective classification method where (i) collective classification allows us to investigate relations among several mentions and autocorrelation among IS classes and (ii) cascaded classification allows us to tackle class imbalance, important for minority classes such as bridging. We show that our method outperforms current methods both for IS recognition overall as well as for bridging, specifically. The second stage, bridging antecedent selection, finds the antecedents for all predicted bridging anaphors. We investigate the phenomenon of semantically or syntactically related bridging anaphors that share the same antecedent, a phenomenon we call sibling anaphors. We show that taking sibling anaphors into account in a joint inference model improves antecedent selection performance. In addition, we develop semantic and salience features for antecedent selection and suggest a novel method to build the candidate antecedent list for an anaphor, using the discourse scope of the anaphor. Our model outperforms previous work significantly.

Name Disambiguation by Collective Classification

2014 ◽  
pp. 406-417
Author(s):  
Zhongxiang Chen ◽  
Jiafeng Guo ◽  
Yanyan Lan ◽  
Lei Cao ◽  
Xueqi Cheng
Keyword(s):  
Name Disambiguation ◽  
Collective Classification
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