1997 SemanticTaggingUsingAProbCFG

Subject Headings: Semantic Annotation Task.

Notes

(Zhao and Grishman, 2005) ⇒ Shubin Zhao, and Ralph Grishman. (2005). “Extracting Relations with Integrated Information Using Kernel Methods.” In: Proceedings of ACL Conference (ACL 2005).
- Collins et al. (1997) and Miller et al. (2000) used statistical parsing models to extract relational facts from text, which avoided pipeline processing of data. However, their results are essentially based on the output of sentence parsing, which is a deep processing of text. So their approaches are vulnerable to errors in parsing. Collins et al. (1997) addressed a simplified task within a confined context in a target sentence.

This paper describes a statistical model for extraction of events at the sentence level, or “semantic tagging", typically the first level of processing in Information Extraction systems. We illustrate the approach using a management succession task, tagging sentences with three slots involved in each succession event: the post, person coming into the post, and person leaving the post. The approach requires very limited resources: a part-of-speech tagger; a morphological analyzer; and a set of training examples that have been labeled with the three slots and the indicator (verb or noun) used to express the event. Training on 560 sentences, and testing on 356 sentences, shows the accuracy of the approach is 77.5% (if partial slot matches are deemed incorrect) or 87.8% (if partial slot matches are deemed correct).

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1997 SemanticTaggingUsingAProbCFG	Michael Collins Scott Miller			Semantic Tagging Using a Probabilistic Context Free Grammar		Proceedings of the 6th Workshop on Very Large Corpora	http://acl.ldc.upenn.edu/W/W98/W98-1105.pdf			1997