Practical solutions to the problem of diagonal dominance in kernel document clustering

By Derek Greene, Padraig Cunningham. 2006

723 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Percy Liang
2. Jianfeng Gao
3. Quoc V. Le
4. Mohit Bansal
5. Yoshua Bengio
6. Trevor Darrell
7. Fei Sha
8. Erik Cambria

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Trevor Darrell
3. Dacheng Tao
4. Ming-Hsuan Yang
5. Xiaogang Wang
6. Jitendra Malik
7. Yi Yang
8. Alan L. Yuille

Citations in the corpus

• Larger-Context Language Modelling with Recurrent Neural Network (2015, methodology): “We use the BBC corpus prepared by Greene & Cunningham (2006).”
• Towards a Seamless Integration of Word Senses into Downstream NLP Applications (2017, background): “10 The BBC news dataset 11 (Greene and Cunningham, 2006) comprises news articles taken from BBC, divided into five topics: business, entertainment, politics, sport and tech.”
• From Word Embeddings To Document Distances (2015, background): “…between two documents has far-reaching applications in document retrieval (Salton & Buckley, 1988), news categorization and clustering (Ontrup & Ritter, 2001; Greene & Cunningham, 2006), song identification (Brochu & Freitas, 2002), and multilingual document matching (Quadrianto et al., 2009).”
• Incomplete Multiview Spectral Clustering With Adaptive Graph Learning (2020, background): “7) BBCSport Database [59]: The original BBCSport database contains 737 documents about the sport news articles collected from the BBC Sport website.”
• Gated Word-Character Recurrent Language Model (2016, methodology): “Ling et al. (2015) introduce the compositional character-to-word (C2W) model that takes as input character-level representation of a word and generates vector representation of the word using a bidirectional LSTM (Graves and Schmidhuber, 2005).”
• BERTopic: Neural topic modeling with a class-based TF-IDF procedure (2022, methodology): “The BBC News dataset 4 contains 2225 documents from the BBC News website between 2004 and 2005 (Greene and Cunningham, 2006).”
• Supervised Word Mover's Distance (2016, background): “These sparse, high-dimensional representations are fre26 quently nearly orthogonal [17] and a pair of similar documents may therefore have the nearly the 27 same distance as a pair that are very different.”
• Deep Learning--based Text Classification (2020, methodology): “ther datasets derived from different subsets of the Reuters dataset, such as R8, R52, RCV1, and RCV1-v2. Other datasets developed for news categorization includes: Bing news [166], NYTimes [167], BBC [168], Google news [169], to name a few. 3.3 Topic Classification Datasets DBpedia. The DBpedia data”
• Transfer Hashing with Privileged Information (2016, methodology): “To verify the effectiveness of our proposed approaches, ITQ+ and LapITQ+, we conduct a series of experiments on three benchmark datasets: BBC Collection [Greene and Cunningham, 2006], multilingual Reuters [Amini et al., 2009], and NUS-WIDE [Chua et al., 2009].”
• NewsEmbed: Modeling News through Pre-trained Document Representations (2021, methodology): “We consider four popularly used datasets that cover a variety of publishers and news styles: BBC [24], 20 Newsgroups8, AG News [63], and MIND-small [59], with statistics outlined in Table 7.”