Clustering and Diversifying Web Search Results with Graph-Based Word Sense Induction

@article{Marco2013ClusteringAD,
  title={Clustering and Diversifying Web Search Results with Graph-Based Word Sense Induction},
  author={Antonio Di Marco and Roberto Navigli},
  journal={Computational Linguistics},
  year={2013},
  volume={39},
  pages={709-754},
  url={https://api.semanticscholar.org/CorpusID:1775181}
}
Key to the approach is to first acquire the various senses of an ambiguous query and then cluster the search results based on their semantic similarity to the word senses induced, which outperforms both Web clustering and search engines.
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158 Citations
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118 References

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This work first acquires the senses of a query by means of a graph-based clustering algorithm that exploits cycles in the co-occurrence graph of the query, then clusters the search results based on their semantic similarity to the induced word senses.

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