Semantic Tagging using a Probabilistic Context Free Grammar
Michael Collins, Scott L. Miller · 1998
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). 1 Introduction Statistical models have been used quite successfully in natural language processing for recovery of hidden structure such as part-of-speech tags, or syntactic s...