Abstract
The task of main verb identification is to recognize the predicate-verb in a sentence. This task plays a crucial role in various areas such as knowledge acquisition, text mining, and question answering, and is also an important preprocessing for many applications including sentence pattern analysis and semantic roles identification. This paper proposes a domain-independent bootstrapping method to automatically identify main verbs of sentences from un-annotated domain-specific Chinese unstructured texts. Experimental results in two domains show that the algorithm is promising. As applications of the main verb identification, we have developed a main verb driven approach of extracting domain-specific terms from unstructured text corpus.
The first and third authors are supported by the Program for New Century Excellent Talents in Universities of China and the IPv6 based National Foundation Education Grid (the Model Project of China Next Generation Internet) and Beijing Institute of Technology Basic Research Foundation (grant no.411002). The second author is supported by the Natural Science Foundation (grant no.60273019, 60496326, 60573063, and 60573064), and the National 973 Program (grant no. 2003CB317008 and G1999032701).
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Zhang, C., Cao, C., Niu, Z. (2007). A Bootstrapping Approach for Chinese Main Verb Identification. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74819-9_72
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DOI: https://doi.org/10.1007/978-3-540-74819-9_72
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