Paper: Learning Graph Walk Based Similarity Measures for Parsed Text

ACL ID D08-1095
Title Learning Graph Walk Based Similarity Measures for Parsed Text
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2008
Authors

We consider a parsed text corpus as an in- stance of a labelled directed graph, where nodes represent words and weighted directed edges represent the syntactic relations be- tween them. We show that graph walks, com- bined with existing techniques of supervised learning, can be used to derive a task-specific word similarity measure in this graph. We also propose a new path-constrained graph walk method, in which the graph walk process is guided by high-level knowledge about mean- ingful edge sequences (paths). Empirical eval- uation on the task of named entity coordinate term extraction shows that this framework is preferable to vector-based models for small- sized corpora. It is also shown that the path- constrained graph walk algorithm yields both performance and scalability gains.