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High-quality phrase representations are essential to finding topics and related terms in documents (a.k.a.
Matching the blanks: Distributional similarity for relation learning
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Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
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A simple framework for contrastive learning of visual representations
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Latent dirichlet allocation
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Introduction to the conll-2003 shared task: Language-independent named entity recognition
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Empower entity set expansion via language model probing
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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun. 2006 · 2006
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Topic modeling: beyond bag-of-words
Hanna M. Wallach. 2006 · 2006
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Topical n-grams: Phrase and topic discovery, with an application to information retrieval
Xuerui Wang, Andrew McCallum, and Xing Wei. 2007 · 2007
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Reading tea leaves: How humans interpret topic models
Jonathan Chang, Jordan L. Boyd-Graber, Sean Gerrish, Chong Wang, and David M. Blei. 2009 · 2009
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Reformulating unsupervised style transfer as paraphrase generation
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Parsing natural scenes and natural language with recursive neural networks
Richard Socher, Cliff Chiung-Yu Lin, A. Ng, and Christopher D. Manning. 2011 · 2011
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A phrase-discovering topic model using hierarchical pitman-yor processes
Robert V. Lindsey, Will Headden, and Michael Stipicevic. 2012 · 2012
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Clear: Contrastive learning for sentence representation
Zhuofeng Wu, Sinong Wang, Jiatao Gu, Madian Khabsa, Fei Sun, and Hao Ma. 2020 · 2012
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Query understanding enhanced by hierarchical parsing structures
Jingjing Liu, Panupong Pasupat, Yining Wang, D. Scott Cyphers, and James R. Glass. 2013 · 2013
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Automatic construction and ranking of topical keyphrases on collections of short documents
Marina Danilevsky, Chi Wang, Nihit Desai, Xiang Ren, Jingyi Guo, and Jiawei Han. 2014 · 2014
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Scalable topical phrase mining from text corpora
Ahmed El-Kishky, Yanglei Song, Chi Wang, Clare R. Voss, and Jiawei Han. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
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Abstractive multi-document summarization via phrase selection and merging
Lidong Bing, Piji Li, Yi Liao, Wai Lam, Weiwei Guo, and Rebecca J. Passonneau. 2015 · 2015
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Using phrases in mallet topic models
David Mimno. 2015 · 2015
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Deep keyphrase generation
Rui Meng, Sanqiang Zhao, Shuguang Han, Daqing He, Peter Brusilovsky, and Yu Chi. 2017 · 2017
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Self-taught convolutional neural networks for short text clustering
Jiaming Xu, Bo Xu, Peng Wang, Suncong Zheng, Guanhua Tian, and Jun Zhao. 2017 · 2017
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Learning phrase embeddings from paraphrases with grus
Zhihao Zhou, Lifu Huang, and Heng Ji. 2017 · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann Dauphin, and David Lopez-Paz. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Learning composition models for phrase embeddings
Mo Yu and Mark Dredze. 2015 · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Jake Zhao, and Yann André LeCun. 2015 · 2015
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Feuding families and former friends: Unsupervised learning for dynamic fictional relationships
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Biocreative v cdr task corpus: a resource for chemical disease relation extraction
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Embarrassingly simple unsupervised aspect extraction
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Luke: Deep contextualized entity representations with entity-aware self-attention
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Simcse: Simple contrastive learning of sentence embeddings
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Learning dense representations of phrases at scale
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Coco-lm: Correcting and contrasting text sequences for language model pretraining
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Supporting clustering with contrastive learning
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