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Learning graph walk based similarity measures for parsed text.
| Content Provider | CiteSeerX |
|---|---|
| Author | Cohen, William W. Minkov, Einat |
| Abstract | We consider a parsed text corpus as an instance of a labelled directed graph, where nodes represent words and weighted directed edges represent the syntactic relations between them. We show that graph walks, combined 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 meaningful edge sequences (paths). Empirical evaluation on the task of named entity coordinate term extraction shows that this framework is preferable to vector-based models for smallsized corpora. It is also shown that the pathconstrained graph walk algorithm yields both performance and scalability gains. 1 |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Meaningful Edge Sequence Parsed Text Corpus Pathconstrained Graph Empirical Evaluation Smallsized Corpus Graph Walk Process Scalability Gain Algorithm Yield Syntactic Relation Task-specific Word Similarity Measure Supervised Learning Entity Coordinate Term Extraction Labelled Directed Graph New Path-constrained Graph Walk Method Vector-based Model Graph Walk Similarity Measure High-level Knowledge |
| Content Type | Text |
