HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Fully parsing the Penn Treebank
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Lexical and structural biases for function parsing
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Accurate learning for Chinese function tags from minimal features
ACLstudent '09 Proceedings of the ACL-IJCNLP 2009 Student Research Workshop
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Function tags are a context-sensitive annotation applied to words and phrases of natural language text, marking their syntactic or semantic role within a larger utterance. As researchers improve results on various other problems in “pure” natural language processing (e.g part-of-speech tagging, parsing), those who work in the more “applied” NLP fields (e.g. question-answering, temporal analysis) are seeking more powerful sorts of linguistic annotation as input for their own systems. Hence, function tags. In the first part of the thesis, I present the problem of function tagging: why it is an interesting problem, who has worked on similar thing, and what exactly I intend to do. I briefly review the function tags of the Penn treebank, and explain the specific metrics by which I will evaluate my work. In the second part of the thesis, I introduce the many features that I will use to train a function tagging system, and then I present some systems that make use of them: one using feature trees, one using decision trees (briefly), and one using perceptron models. For each system, I give a brief historical perspective, an overview of where it has been used before and why I think it will be useful in this task. I will then try a number of feature combinations with interesting properties; and finally, present the best-performing tweaked-out version of that system. Finally, in the third part of the thesis, I bring them all together and discuss the advantages and disadvantages of each system in various situations. More interestingly, I will present an analysis of what features prove to be the most helpful for the different function tagging subtasks. Lastly, I will present a comparison to other systems performing related tasks, and speculate on some interesting future work.