Learning Parse and Translation Decisions from Examples with Rich Context

Learning Parse and Translation Decisions From Examples With Rich Context Ulf Hermjakob and Raymond J. Mooney Dept. of Computer Sciences University of Texas at Austin Austin, TX 78712, USA ulf@cs.utexas.edu mooney@cs.utexas.edu Abstract We present a knowledge and context-based system for parsing and translating natu- ral language and evaluate it on sentences from the Wall Street Journal. Applying machine learning techniques, the system uses parse action examples acquired un- der supervision to generate a determinis- tic shift-reduce parser in the form of a de- cision structure. It relies heavily on con- text, as encoded in features which describe the morphological, syntactic, semantic and other aspects of a given parse state. 1 Introduction The parsing of unrestricted text, with its enormous lexical and structural ambiguity, still poses a great challenge in natural language processing. The tradi- tional approach of trying to master the complexity of parse grammars with hand-coded rules turned out to be much more difficult than expected, if not impos- sible. Newer statistical approaches with often only very limited context sensitivity seem to have hit a performance ceiling even when trained on very large corpora. To cope with the complexity of unrestricted text, parse rules in any kind of formalism will have to consider a complex context with many different mor- phological, syntactic or semantic features. This can present a significant problem, because even linguisti- cally trained natural language developers have great difficulties writing and even more so extending ex- plicit parse grammars covering a wide range of nat- ural language. On the other hand it is much easier for humans to decide how specific sentences should be analyzed. We therefore propose an approach to parsing based on learning from examples with a very strong emphasis on context, integrating morphological, syntactic, semantic and other aspects relevant to making good parse decisions, thereby also allowing the parsing to be deterministic. Applying machine learning techniques, the system uses parse action ex- amples acquired under supervision to generate a de- terministic shift-reduce type parser in the form of a decision structure. The generated parser transforms input sentences into an integrated phrase-structure and case-frame tree, powerful enough to be fed into a transfer and a generation module to complete the full process of machine translation. Balanced by rich context and some background knowledge, our corpus based approach relieves the NL-developer from the hard if not impossible task of writing explicit grammar rules and keeps grammar coverage increases very manageable. Compared with standard statistical methods, our system relies on deeper analysis and more supervision, but radically fewer examples. 2 Basic Parsing Paradigm As the basic mechanism for parsing text into a shallow semantic representation, we choose a shift- reduce type parser (Marcus, 1980). It breaks parsing into an ordered sequence of small and manageable parse actions such as shift and reduce. This ordered 'left-to-right' parsing is much closer to how humans parse a sentence than, for example, chart oriented parsers; it allows a very transparent control struc- ture and makes the parsing process relatively intu- …

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