LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
Random-walk term weighting for improved text classification
TextGraphs-1 Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing
Smoothing document language model with local word graph
Proceedings of the 18th ACM conference on Information and knowledge management
Graph-based term weighting for information retrieval
Information Retrieval
Fixed versus dynamic co-occurrence windows in TextRank term weights for information retrieval
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Experiments on pseudo relevance feedback using graph random walks
SPIRE'12 Proceedings of the 19th international conference on String Processing and Information Retrieval
Graph-of-word and TW-IDF: new approach to ad hoc IR
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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We present a way of estimating term weights for Information Retrieval (IR), using term co-occurrence as a measure of dependency between terms.We use the random walk graph-based ranking algorithm on a graph that encodes terms and co-occurrence dependencies in text, from which we derive term weights that represent a quantification of how a term contributes to its context. Evaluation on two TREC collections and 350 topics shows that the random walk-based term weights perform at least comparably to the traditional tf-idf term weighting, while they outperform it when the distance between co-occurring terms is between 6 and 30 terms.