Learning to classify short and sparse text & web with hidden topics from large-scale data collections

By Xuan Hieu Phan, Minh Le Nguyen, Susumu Horiguchi. The Web Conference, 2008

902 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8907ed38d6f9c56aa406704c7354afd2472d364e

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Citations in the corpus

Semantic Clustering and Convolutional Neural Network for Short Text Categorization (2015, methodology): “Experiments are conducted on two benchmarks: Google Snippets (Phan et al. , 2008) and TREC (Li and Roth, 2002).”
A biterm topic model for short texts (2013, background): “Phan et al.[24] learned hidden topics from large external resources to enrich the representation of short texts.”
Short text classification in twitter to improve information filtering (2010, background): “Automatic text classification and hidden topic extraction [5] approaches perform well when there is meta-information or the context of the short text is extended with knowledge extracted using large collections.”
On Stopwords, Filtering and Data Sparsity for Sentiment Analysis of Twitter (2014, positive): “Dataset sparsity is an important factor that affects the overall performance of a typical machine learning classifier (Phan et al., 2008).”
Composition in Distributional Models of Semantics (2010, background): “In fact, this is explicit in the work of Plate (1991) who refers to his architecture as a convolution memory and also in the name recursive auto-associative memory chosen by Pollack (1990).”
topicmodels: An R Package for Fitting Topic Models (2011, methodology): “GibbsLDA++ (Phan, Nguyen, and Horiguchi 2008) is available under the GPL from http://gibbslda.sourceforge.net/ .”
Exploiting Wikipedia as external knowledge for document clustering (2009, background): “Phan et al. [ 9 ] present a framework for discovering hidden topics from large-scale data collections to resolve the data sparsity problem in short text classification.”
Evaluation Datasets for Twitter Sentiment Analysis: A survey and a new dataset, the STS-Gold (2013, background): “Dataset sparsity is an important factor that affects the overall performance of typical machine learning classifiers [17].”
A Survey on Deep Semi-Supervised Learning (2021, background): “Answers [106], IMDB [107], Yelp review [104], Snippets [108], Ohsumed [109], TagMyNews [110], MR [111], Elec [112], Rotten Tomatoes [111], RCV1 [113] [114], [115], [116], [117], [118], [119] Dialogue policy learning MultiWOZ [120] [121] Dialogue generation Cornell Movie Dialogs Corpus [122], Ubuntu ”
Short Text Topic Modeling Techniques, Applications, and Performance: A Survey (2019, background): “Earlier works [15], [16] still used traditional topic models for short texts, but exploited external knowledge or metadata to bring in additional useful word co-occurrences across short texts, and therefore may boost the performance of topic models.”
Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification (2019, background): “Firstly, short texts are usually semantically sparse and ambiguous, lacking contexts (Phan et al., 2008).”
BTM: Topic Modeling over Short Texts (2014, background): “For example, Phan et al.[22] inferred the topics of short texts based on a conventional topic model estimated on another large scale dataset for short text classification.”
SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation (2014, background): “Models of association, on the other hand, may be better suited to tasks such as word-sense disambiguation (Navigli, 2009), and applications such as text classiﬁcation (Phan et al., 2008) in which the target classes correspond to topical domains such as agriculture or sport (Rose et al., 2002).”
Topic Modeling for Short Texts with Auxiliary Word Embeddings (2016, methodology): “Phan et al. proposed to infer topic structure of short texts by using the learnt latent topics from Wikipedia [28].”