Title : 
Research of Event Pronoun Resolution
         
        
            Author : 
Zhang Ning ; Kong Fang ; Li Peifeng
         
        
            Author_Institution : 
JiangSu Provincial Key Lab. for Comput. Inf. Process. Technol., China
         
        
        
        
        
        
            Abstract : 
Event anaphora resolution plays an important role in discourse analysis. In comparison with general noun phrases, pronouns carry little information of themselves, resolving the event pronouns is a more difficult task. This paper proposes a machine learning-based framework for event pronoun resolution. All kinds of features, including both flat and structural features, are explored for event pronoun resolution. Experiments on OntoNotes corpus show that both flat and structural features are very effective for this task.
         
        
            Keywords : 
learning (artificial intelligence); discourse analysis; event anaphora resolution; event pronoun resolution; machine learning-based framework; structural features; Computational linguistics; Convolution; Kernel; Natural language processing; Semantics; Syntactics; Training; Event pronoun resolution; Features selection; Instances creation;
         
        
        
        
            Conference_Titel : 
Asian Language Processing (IALP), 2011 International Conference on
         
        
            Conference_Location : 
Penang
         
        
            Print_ISBN : 
978-1-4577-1733-8
         
        
        
            DOI : 
10.1109/IALP.2011.31