RichVSM: enRiched vector space models for folksonomies
Proceedings of the 20th ACM conference on Hypertext and hypermedia
GeoFolk: latent spatial semantics in web 2.0 social media
Proceedings of the third ACM international conference on Web search and data mining
Word sense disambiguation-based sentence similarity
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Query classification using Wikipedia
International Journal of Intelligent Information and Database Systems
Query expansion in folksonomies
SAMT'10 Proceedings of the 5th international conference on Semantic and digital media technologies
Latent Geospatial Semantics of Social Media
ACM Transactions on Intelligent Systems and Technology (TIST)
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In natural languages, variability of semantic expression refers to the situation where the same meaning can be inferred from different words or texts. Given that many natural language processing tasks nowadays (e.g. question answering, information retrieval, document summarization) often model this variability by requiring a specific targetmeaning to be inferred from different text variants, it is helpful to capture text similarity in a directional manner to serve such inference needs. In this paper, we show how Wikipedia can be used as a semantic resource to build a directional inferential similarity metric between words, and subsequently, texts. Through experiments, we show that our Wikipedia-based metric performs significantly better when applied to a standard evaluation dataset, with a reduction in error rate of 16.1% over the random metric baseline.