Paper: Low-Rank Tensors for Scoring Dependency Structures

ACL ID P14-1130
Title Low-Rank Tensors for Scoring Dependency Structures
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Accurate scoring of syntactic structures such as head-modifier arcs in dependency parsing typically requires rich, high- dimensional feature representations. A small subset of such features is often se- lected manually. This is problematic when features lack clear linguistic meaning as in embeddings or when the information is blended across features. In this paper, we use tensors to map high-dimensional fea- ture vectors into low dimensional repre- sentations. We explicitly maintain the pa- rameters as a low-rank tensor to obtain low dimensional representations of words in their syntactic roles, and to leverage mod- ularity in the tensor for easy training with online algorithms. Our parser consistently outperforms the Turbo and MST parsers across 14 different languages. We also ob- tain th...