Shallow Parsing as Part-of-Speech Tagging

In: Proceedings of CoNLL-2000 and LLL-2000, pages 145-147, Lisbon, Portugal, 2000. Shallow Parsing as Part-of-Speech Tagging* Miles Osborne University of Edinburgh Division of Informatics 2 Buccleuch Place Edinburgh EH8 9LW , Scotland osborne@cogsci, ed. ac. uk Abstract Treating shallow parsing as part-of-speech tag- ging yields results comparable with other, more elaborate approaches. Using the CoNLL 2000 training and testing material, our best model had an accuracy of 94.88%, with an overall FB1 score of 91.94%. The individual FB1 scores for NPs were 92.19%, VPs 92.70% and PPs 96.69%. 1 Introduction Shallow parsing has received a reasonable amount of attention in the last few years (for example (Ramshaw and Marcus, 1995)). In this paper, instead of modifying some existing technique, or else proposing an entirely new ap- proach, we decided to build a shallow parser us- ing an off-the-shelf part-of-speech (POS) tagger. We deliberately did not modify the POS tag- ger's internal operation in any way. Our results suggested that achieving reasonable shallow- parsing performance does not in general require anything more elaborate than a simple POS tag- ger. However, an error analysis suggested the existence of a small set of constructs that are not so easily characterised by finite-state ap- proaches such as ours. 2 The Tagger We used Ratnaparkhi's maximum entropy- based POS tagger (Ratnaparkhi, 1996). When tagging, the model tries to recover the most likely (unobserved) tag sequence, given a se- quence of observed words. For our experiments, we used the binary-only distribution of the tagger (Ratnaparkhi, 1996). " The full version of this paper can be found at http://www.cogsci.ed.ac.uk/'osborne/shallow.ps 3 Convincing the Tagger to Shallow Parse The insight here is that one can view (some of) the differences between tagging and (shal- low) parsing as one of context: shallow pars- ing requires access to a greater part of the surrounding lexical/POS syntactic environment than does simple POS tagging. This extra in- formation can be encoded in a state. However, one must balance this approach with the fact that as the amount of information in a state increases, with limited training ma- terial, the chance of seeing such a state again in the future diminishes. We therefore would expect performance to increase as we increased the amount of information in a state, and then decrease when overfitting and/or sparse statis- tics become dominate factors. We trained the tagger using 'words' that were various 'configurations' (concatenations) of ac- tual words, POS tags, chunk-types, and/or suf- fixes or prefixes of words and/or chunk-types. By training upon these concatenations, we help bridge the gap between simple POS tagging and shallow parsing. In the rest of the paper, we refer to what the tagger considers to be a word as a configura- tion. A configuration will be a concatenation of various elements of the training set relevant to decision making regarding chunk assignment. A 'word' will mean a word as found in the train- ing set. 'Tags' refer to the POS tags found in the training set. Again, such tags may be …

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