Short version (8 pages)
download from file://ftp.cs.utexas.edu/pub/mooney/papers/contex-acl-97.ps.gz

download from alternative site http://www.isi.edu/~ulf/papers/contex-acl-97.ps.gz
download from alternative site http://www.isi.edu/~ulf/papers/contex-acl-97.pdf

Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics (ACL-97), pp. 482-489

The full Ph.D. thesis (x+165 pages)
download from file://ftp.cs.utexas.edu/pub/techreports/tr97-12.ps.gz

download from alternative site http://www.isi.edu/~ulf/papers/hermjakob-dissertation-97.ps.gz
download from alternative site http://www.isi.edu/~ulf/papers/hermjakob-dissertation-97.pdf

Also available as Tech Report 97-12, Department of Computer Sciences, University of Texas at Austin

Dissertation abstract:

The parsing of unrestricted text, with its enormous lexical and structural ambiguity, still poses a great challenge in natural language processing. The difficulties with traditional approaches, which try to master the complexity of parse grammars with hand-crafted rules, have led to a trend towards more empirical techniques.

We therefore propose a system for parsing and translating natural language that learns from examples and uses some background knowledge. As our parsing model we choose a deterministic shift-reduce type parser that integrates part-of-speech tagging and syntactic and semantic processing, which not only makes parsing very efficient, but also assures transparency during the supervised example acquisition. Applying machine learning techniques, the system uses parse action examples to generate a parser in the form of a decision structure, a generalization of decision trees. To learn good parsing and translation decisions, our system relies heavily on context, as encoded in currently 205 features describing the morphological, syntactical and semantical aspects of a given parse state. Compared with recent probabilistic systems that were trained on 40,000 sentences, our system relies on more background knowledge and a deeper analysis, but radically fewer examples, currently 256 sentences.

We test our parser on lexically limited sentences from the Wall Street Journal and achieve accuracy rates of 89.8% for labeled precision, 98.4% for part of speech tagging and 56.3% of test sentences without any crossing brackets.

Machine translations of 32 Wall Street Journal sentences to German have been evaluated by 10 bilingual volunteers and been graded as 2.4 on a 1.0 (best) to 6.0 (worst) scale for both grammatical correctness and meaning preservation. The translation quality was only minimally better (2.2) when starting each translation with the correct parse tree, which indicates that the parser is quite robust and that its errors have only a moderate impact on final translation quality.

These parsing and translation results already compare well with other systems and, given the relatively small training set and amount of overall knowledge used so far, the results suggest that our system CONTEX can break previous accuracy ceilings when scaled up further.

Dissertation committee:

Raymond J. Mooney, supervisor, Dept. of Computer Sciences, mooney@cs.utexas.edu
Benjamin J. Kuipers, Dept. of Computer Sciences, kuipers@cs.utexas.edu
Risto Miikkulainen, Dept. of Computer Sciences, risto@cs.utexas.edu
Manfred Krifka, Dept. of Linguistics, krifka@mail.utexas.edu
Elaine A. Rich, head of former Knowledge Based Natural Language group at MCC, ear@cs.utexas.edu