Video events recognition is a challenging task for high-level understanding of video se- quence. At present, there are two major limitations in existing methods for events recognition. One is that no algorithms are av...Video events recognition is a challenging task for high-level understanding of video se- quence. At present, there are two major limitations in existing methods for events recognition. One is that no algorithms are available to recognize events which happen alternately. The other is that the temporal relationship between atomic actions is not fully utilized. Aiming at these problems, an algo- rithm based on an extended stochastic context-free grammar （SCFG） representation is proposed for events recognition. Events are modeled by a series of atomic actions and represented by an extended SCFG. The extended SCFG can express the hierarchical structure of the events and the temporal re- lationship between the atomic actions. In comparison with previous work, the main contributions of this paper are as follows： ① Events （include alternating events） can be recognized by an improved stochastic parsing and shortest path finding algorithm. ② The algorithm can disambiguate the detec- tion results of atomic actions by event context. Experimental results show that the proposed algo- rithm can recognize events accurately and most atomic action detection errors can be corrected sim- ultaneously.展开更多
Event extraction is an important research point in information extraction, which includes two important sub-tasks of event type recognition and event argument recognition. This paper describes a method based on automa...Event extraction is an important research point in information extraction, which includes two important sub-tasks of event type recognition and event argument recognition. This paper describes a method based on automatic expansion of the event triggers for event type recognition. The event triggers are first extended through a thesaurus to enable the extraction of the candidate events and their candidate types. Then, a binary classification method is used to recognize the candidate event types. This method effectively improves the unbalanced data problem in training models and the data sparseness problem with a small corpus. Evaluations on the ACE2005 dataset give a final F-score of 61.24%, which outperforms traditional methods based on pure machine learning.展开更多
基金Supported by the National Natural Science Foundation of China(60805028,60903146)Natural Science Foundation of Shandong Province of China (ZR2010FM027)+1 种基金SDUST Research Fund(2010KYTD101)China Postdoctoral Science Foundation(2012M521336)
文摘Video events recognition is a challenging task for high-level understanding of video se- quence. At present, there are two major limitations in existing methods for events recognition. One is that no algorithms are available to recognize events which happen alternately. The other is that the temporal relationship between atomic actions is not fully utilized. Aiming at these problems, an algo- rithm based on an extended stochastic context-free grammar （SCFG） representation is proposed for events recognition. Events are modeled by a series of atomic actions and represented by an extended SCFG. The extended SCFG can express the hierarchical structure of the events and the temporal re- lationship between the atomic actions. In comparison with previous work, the main contributions of this paper are as follows： ① Events （include alternating events） can be recognized by an improved stochastic parsing and shortest path finding algorithm. ② The algorithm can disambiguate the detec- tion results of atomic actions by event context. Experimental results show that the proposed algo- rithm can recognize events accurately and most atomic action detection errors can be corrected sim- ultaneously.
基金Supported by the National Natural Science Foundation of China(Nos. 60975055 and 60803093)the National High-Tech Research and Development (863) Program of China (No.2008AA01Z144)
文摘Event extraction is an important research point in information extraction, which includes two important sub-tasks of event type recognition and event argument recognition. This paper describes a method based on automatic expansion of the event triggers for event type recognition. The event triggers are first extended through a thesaurus to enable the extraction of the candidate events and their candidate types. Then, a binary classification method is used to recognize the candidate event types. This method effectively improves the unbalanced data problem in training models and the data sparseness problem with a small corpus. Evaluations on the ACE2005 dataset give a final F-score of 61.24%, which outperforms traditional methods based on pure machine learning.
