Clustering short texts using wikipedia

By Somnath Banerjee, Krishnan Ramanathan, Ajay Gupta. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2007

383 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Communication, Information Systems

View Paper: https://www.semanticscholar.org/paper/a8b1d46c09e101b31cfb263aa5dc63863b038410

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Citations in the corpus

Data clustering: 50 years beyond K-means (2008, neutral): “Banerjee et al. (2007a) present a family of multi-way clustering schemes that is applicable to a class of loss functions known as Bregman divergences.”
Mining Meaning from Wikipedia (2008, positive): “Wikipedia’s ability to improve classiﬁcation of short documents is conﬁrmed by Banerjee et al. (2007), who cluster news articles under feed items such as those provided by Google News.”
A dirichlet multinomial mixture model-based approach for short text clustering (2014, methodology): “Similar to [2], we utilize Google News 1 as one of the labeled datasets to evaluate the clustering performance.”
Learning to classify short and sparse text & web with hidden topics from large-scale data collections (2008, methodology): “Another way is to utilize online data repositories, such as Wikipedia or Open Directory Project(1), as external knowledge sources [4, 31].”
Fast and Accurate Annotation of Short Texts with Wikipedia Pages (2010, methodology): “One approach consists of extending the classic term-based vector-space model with additional dimensions corresponding to features (concepts) extracted from an external knowledge base, such as DMOZ [4, 6], Wikipedia [7, 1, 9], or even the whole Web (such as the Google’s kernel [15]).”
Learning to link with wikipedia (2008, methodology): “It is fast becoming the resource of choice for such tasks, and has been applied to text categorization (Gabrilovich and Markovich 2007), indexing (Medelyan et al. 2008), clustering (Banerjee et al. 2007), searching (Milne et al. 2007), and a host of other problems.”
Short text classification in twitter to improve information filtering (2010, background): “Existing works on classification of short text messages integrate messages with meta-information from other information sources such as Wikipedia and WordNet [ 2 ,3].”
Self-Taught Convolutional Neural Networks for Short Text Clustering (2017, methodology): “For example, Banerjee et al. [4] proposed a method of improving the accuracy of short text clustering by enriching their representation with additional features from Wikipedia, and Fodeh et al. [5] incorporate semantic knowledge from an ontology into text clustering.”