Random-Walk Term Weighting for Improved Text Classification
By Samer Hassan, Rada Mihalcea, Carmen Banea. 2006
137 citations recorded by the source corpus.
This paper describes a new approach for estimating term weights in a document, and shows how the new weighting scheme can be used to improve the accuracy of a text classifier. The method uses term co-occurrence as a measure of dependency between word features. A random-walk model is applied on a graph encoding words and co-occurrence dependencies, resulting in scores that represent a quantification of how a particular word feature contributes to a given context. Experiments performed on three standard classification datasets show that the new random-walk based approach outperforms the traditional term frequency approach of feature weighting.
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- François Rousseau
- Michalis Vazirgiannis
- Emmanouil Kiagias
- Zhuyun Dai
- Roi Blanco
- Christina Lioma
- Jamie Callan
- Fragkiskos D. Malliaros
Citations in the corpus
- Text Categorization as a Graph Classification Problem (2015, methodology): “Similarly to (Hassan et al., 2007) that used graphof-words to propose alternative weights for the ngram features, we can capitalize on main core retention to still extract binary n-gram features for classification but considering only the terms belonging to the main core of each document.”
- Graph-based term weighting for information retrieval (2012, methodology): “…(Erkan and Radev 2004), variants and extensions of which have been applied to keyword detection, word sense disambiguation, text classification (Hassan and Banea 2006), summarisation (by extraction or query-biased Esuli and Sebastiani 2007, or ontology-based Plaza et al. 2008), novelty…”