Abstract
In this paper, we investigate a semi-supervised method, that is, self-training algorithm in event detection and type recognition. First, we get two kinds of feature vectors of sentences for event detection and type recognition task respectively. Second, we use self-training algorithm to filter the non-event instances and identify type for every event instance. The experimental result on the ACE2005 corpus shows that our model using self-training algorithm can identify the majority of non-event instances and also has a good precision and recall rate in event type recognition.
This work is supported by NSF Grant #60803078.
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© 2011 Springer-Verlag Berlin Heidelberg
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Yang, X., Chen, J., Lin, R. (2011). Event Detection and Type Recognition Using Self-training. In: Zhang, J. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23226-8_2
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DOI: https://doi.org/10.1007/978-3-642-23226-8_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23225-1
Online ISBN: 978-3-642-23226-8
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