tailieunhanh - Báo cáo khoa học: "Learning Parse and Translation Decisions From Examples With Rich Context"

We present a knowledge and context-based system for parsing and translating natural language and evaluate it on sentences from the Wall Street Journal. Applying machine learning techniques, the system uses parse action examples acquired under supervision to generate a deterministic shift-reduce parser in the form of a decision structure. It relies heavily on context, as encoded in features which describe the morphological, syntactic, semantic and other aspects of a given parse state. | 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@ mooney@ Abstract We present a knowledge and context-based system for parsing and translating natural language and evaluate it on sentences from the Wall Street Journal. Applying machine learning techniques the system uses parse action examples acquired under supervision to generate a deterministic shift-reduce parser in the form of a decision structure. It relies heavily on context 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 traditional 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 impossible. 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 morphological syntactic or semantic features. This can present a significant problem because even linguistically trained natural language developers have great difficulties writing and even more so extending explicit parse grammars covering a wide range of natural 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 .