暂无分享,去创建一个
Yu Chen | Zhenming Liu | Christopher G. Brinton | Adam Hare | Yinan Liu | Zhenming Liu | Yinan Liu | Adam Hare | Yu Chen
[1] Steven Bird,et al. NLTK: The Natural Language Toolkit , 2002, ACL 2006.
[2] Jeff Johnson,et al. Billion-Scale Similarity Search with GPUs , 2017, IEEE Transactions on Big Data.
[3] Richard Socher,et al. Learned in Translation: Contextualized Word Vectors , 2017, NIPS.
[4] Leonidas J. Guibas,et al. A metric for distributions with applications to image databases , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).
[5] W. Torgerson. Multidimensional scaling: I. Theory and method , 1952 .
[6] Edward Chlebus,et al. Estimating parameters of the Pareto distribution by means of Zipf's law: application to Internet research , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..
[7] Anne-Lise Veuthey,et al. Combining NLP and probabilistic categorisation for document and term selection for Swiss-Prot medical annotation , 2003, ISMB.
[8] Hojjat Adeli,et al. Permutation Jaccard Distance-Based Hierarchical Clustering to Estimate EEG Network Density Modifications in MCI Subjects , 2018, IEEE Transactions on Neural Networks and Learning Systems.
[9] Lahomtoires d'Electronique. AN INFORMATIONAL THEORY OF THE STATISTICAL STRUCTURE OF LANGUAGE 36 , 2010 .
[10] Yuen Ren Chao,et al. Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology , 1950 .
[11] Thomas M Cover,et al. Differential Entropy , 2014 .
[12] Wentian Li,et al. Random texts exhibit Zipf's-law-like word frequency distribution , 1992, IEEE Trans. Inf. Theory.
[13] Matt J. Kusner,et al. From Word Embeddings To Document Distances , 2015, ICML.
[14] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[15] S. Niwattanakul,et al. Using of Jaccard Coefficient for Keywords Similarity , 2022 .
[16] Michael C. Hout,et al. Multidimensional Scaling , 2003, Encyclopedic Dictionary of Archaeology.
[17] David P. Williamson,et al. The Design of Approximation Algorithms , 2011 .
[18] Karl Pearson F.R.S.. LIII. On lines and planes of closest fit to systems of points in space , 1901 .
[19] David M. W. Powers,et al. Applications and Explanations of Zipf’s Law , 1998, CoNLL.
[20] Stergios B. Fotopoulos,et al. All of Nonparametric Statistics , 2007, Technometrics.
[21] Benoit B. Mandelbrot,et al. Fractal Geometry of Nature , 1984 .
[22] Guokun Lai,et al. RACE: Large-scale ReAding Comprehension Dataset From Examinations , 2017, EMNLP.
[23] Kumiko Tanaka-Ishii,et al. Do neural nets learn statistical laws behind natural language? , 2017, PloS one.
[24] Mark Chen,et al. Language Models are Few-Shot Learners , 2020, NeurIPS.
[25] Eduardo Laber,et al. Speeding up Word Mover's Distance and its variants via properties of distances between embeddings , 2020, ECAI.
[26] Richard Socher,et al. An Analysis of Neural Language Modeling at Multiple Scales , 2018, ArXiv.
[27] G. Zipf. The Psycho-Biology Of Language: AN INTRODUCTION TO DYNAMIC PHILOLOGY , 1999 .
[28] Sarah Filippi,et al. Optimism in reinforcement learning and Kullback-Leibler divergence , 2010, 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[29] H. Kucera,et al. Computational analysis of present-day American English , 1967 .
[30] Qi Kang,et al. Drifted Twitter Spam Classification Using Multiscale Detection Test on K-L Divergence , 2019, IEEE Access.
[31] Jeffrey Dean,et al. Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.
[32] S. Piantadosi. Zipf’s word frequency law in natural language: A critical review and future directions , 2014, Psychonomic Bulletin & Review.
[33] Steven Bird,et al. NLTK: The Natural Language Toolkit , 2002, ACL.
[34] Y. Takane,et al. Multidimensional Scaling I , 2015 .
[35] Jacques Savoy,et al. Feature selections for authorship attribution , 2013, SAC '13.
[36] Huaiyu Zhu. On Information and Sufficiency , 1997 .
[37] Barnabás Póczos,et al. Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions , 2011, UAI.
[38] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[39] Francis Jack Smith,et al. Extension of Zipf’s Law to Words and Phrases , 2002, COLING.
[40] Daphne Koller,et al. Toward Optimal Feature Selection , 1996, ICML.
[41] Mi Zhou,et al. Centroid Estimation Based on Symmetric KL Divergence for Multinomial Text Classification Problem , 2018, 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA).
[42] Jeffrey Pennington,et al. GloVe: Global Vectors for Word Representation , 2014, EMNLP.
[43] N. Given. Entropy-Based Authorship Search in Large Document Collections , 2006 .
[44] Luke S. Zettlemoyer,et al. Deep Contextualized Word Representations , 2018, NAACL.
[45] Justin Zobel,et al. Using Relative Entropy for Authorship Attribution , 2006, AIRS.
[46] Tomas Mikolov,et al. Enriching Word Vectors with Subword Information , 2016, TACL.
[47] Peter E. Latham,et al. Zipf’s Law Arises Naturally When There Are Underlying, Unobserved Variables , 2016, PLoS Comput. Biol..
[48] Raihana Ferdous,et al. An efficient k-means algorithm integrated with Jaccard distance measure for document clustering , 2009, 2009 First Asian Himalayas International Conference on Internet.
[49] Andrei Z. Broder,et al. On the resemblance and containment of documents , 1997, Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No.97TB100171).
[50] Yading Yuan,et al. Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance , 2017, IEEE Transactions on Medical Imaging.
[51] Vladimir I. Levenshtein,et al. Binary codes capable of correcting deletions, insertions, and reversals , 1965 .
[52] G. Crooks. On Measures of Entropy and Information , 2015 .