Paper: Improved Large Margin Dependency Parsing Via Local Constraints And Laplacian Regularization

ACL ID W06-2904
Title Improved Large Margin Dependency Parsing Via Local Constraints And Laplacian Regularization
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2006
Authors

We present an improved approach for learning dependency parsers from tree- bank data. Our technique is based on two ideas for improving large margin train- ing in the context of dependency parsing. First, we incorporate local constraints that enforce the correctness of each individ- ual link, rather than just scoring the global parse tree. Second, to cope with sparse data, we smooth the lexical parameters ac- cording to their underlying word similar- ities using Laplacian Regularization. To demonstrate the bene ts of our approach, we consider the problem of parsing Chi- nese treebank data using only lexical fea- tures, that is, without part-of-speech tags or grammatical categories. We achieve state of the art performance, improving upon current large margin approaches.