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Learning to walk structured text networks.
| Content Provider | CiteSeerX |
|---|---|
| Abstract | We propose representing a text corpus as a labeled directed graph, where nodes represent words and weighted edges represent the syntactic relations between them, as derived by dependency parsing. Given this graph, we adopt a graph-based similarity measure based on random walks to derive a similarity measure between words, and also use supervised learning to improve the derived similarity measure for a particular task. Empirical evaluation of the approach on the task of coordinate term extraction shows that the suggested framework improves on a state-of-theart distributional similarity measure. 1 |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Coordinate Term Extraction Particular Task Empirical Evaluation Dependency Parsing Syntactic Relation Directed Graph Random Walk Derived Similarity Measure Structured Text Network Text Corpus State-of-theart Distributional Similarity Measure Graph-based Similarity Measure Similarity Measure Suggested Framework |
| Content Type | Text |
