On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysis
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[1] Christopher D. Manning,et al. Bilingual Word Embeddings for Phrase-Based Machine Translation , 2013, EMNLP.
[2] Ken Lang,et al. NewsWeeder: Learning to Filter Netnews , 1995, ICML.
[3] Timothy Baldwin,et al. Multiword Expressions: A Pain in the Neck for NLP , 2002, CICLing.
[4] Alexander M. Rush,et al. Character-Aware Neural Language Models , 2015, AAAI.
[5] Iryna Gurevych,et al. Supersense Embeddings: A Unified Model for Supersense Interpretation, Prediction, and Utilization , 2016, ACL.
[6] Phil Blunsom,et al. A Convolutional Neural Network for Modelling Sentences , 2014, ACL.
[7] Derek Greene,et al. Practical solutions to the problem of diagonal dominance in kernel document clustering , 2006, ICML.
[8] Daniel Jurafsky,et al. Do Multi-Sense Embeddings Improve Natural Language Understanding? , 2015, EMNLP.
[9] Yoon Kim,et al. Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.
[10] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[11] Tong Zhang,et al. Effective Use of Word Order for Text Categorization with Convolutional Neural Networks , 2014, NAACL.
[12] Jason Weston,et al. Question Answering with Subgraph Embeddings , 2014, EMNLP.
[13] Nigel Collier,et al. Towards a Seamless Integration of Word Senses into Downstream NLP Applications , 2017, ACL.
[14] Jörg Kindermann,et al. Text Categorization with Support Vector Machines. How to Represent Texts in Input Space? , 2002, Machine Learning.
[15] Iryna Gurevych,et al. From Text to Lexicon: Bridging the Gap between Word Embeddings and Lexical Resources , 2018, COLING.
[16] Xiang Zhang,et al. Which Encoding is the Best for Text Classification in Chinese, English, Japanese and Korean? , 2017, ArXiv.
[17] José Camacho-Collados,et al. From Word to Sense Embeddings: A Survey on Vector Representations of Meaning , 2018, J. Artif. Intell. Res..
[18] Benjamin Van Durme,et al. Efficient, Compositional, Order-sensitive n-gram Embeddings , 2017, EACL.
[19] Rada Mihalcea,et al. Random-Walk Term Weighting for Improved Text Classification , 2006, International Conference on Semantic Computing (ICSC 2007).
[20] Slav Petrov,et al. Structured Training for Neural Network Transition-Based Parsing , 2015, ACL.
[21] Christopher D. Manning,et al. Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models , 2016, ACL.
[22] Omer Levy,et al. Improving Distributional Similarity with Lessons Learned from Word Embeddings , 2015, TACL.
[23] Fabrizio Sebastiani,et al. Machine learning in automated text categorization , 2001, CSUR.
[24] Jeffrey Pennington,et al. GloVe: Global Vectors for Word Representation , 2014, EMNLP.
[25] Noah A. Smith,et al. Improved Transition-based Parsing by Modeling Characters instead of Words with LSTMs , 2015, EMNLP.
[26] Mihai Surdeanu,et al. The Stanford CoreNLP Natural Language Processing Toolkit , 2014, ACL.
[27] Yann LeCun,et al. Convolutional networks and applications in vision , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.
[28] Wenpeng Yin,et al. Comparative Study of CNN and RNN for Natural Language Processing , 2017, ArXiv.
[29] John Salvatier,et al. Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.
[30] Ming Zhou,et al. A Statistical Parsing Framework for Sentiment Classification , 2014, CL.
[31] Christopher Potts,et al. Learning Word Vectors for Sentiment Analysis , 2011, ACL.
[32] Hinrich Schütze,et al. Nonsymbolic Text Representation , 2016, EACL.
[33] Yann LeCun,et al. Very Deep Convolutional Networks for Text Classification , 2016, EACL.
[34] Yoav Goldberg,et al. A Primer on Neural Network Models for Natural Language Processing , 2015, J. Artif. Intell. Res..
[35] Jonathan Weese,et al. UMBC_EBIQUITY-CORE: Semantic Textual Similarity Systems , 2013, *SEMEVAL.
[36] GunalSerkan,et al. The impact of preprocessing on text classification , 2014 .
[37] Michal Tomana,et al. Influence of Word Normalization on Text Classification , 2007 .
[38] Hinrich Schütze,et al. LAMB: A Good Shepherd of Morphologically Rich Languages , 2016, EMNLP.
[39] Jeffrey Dean,et al. Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.
[40] Yiming Yang,et al. RCV1: A New Benchmark Collection for Text Categorization Research , 2004, J. Mach. Learn. Res..
[41] Thorsten Joachims,et al. Text categorization with support vector machines , 1999 .
[42] Wenpeng Yin,et al. An Exploration of Embeddings for Generalized Phrases , 2014, ACL.
[43] Andrew Y. Ng,et al. Parsing with Compositional Vector Grammars , 2013, ACL.
[44] Cícero Nogueira dos Santos,et al. Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts , 2014, COLING.
[45] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[46] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[47] Bo Pang,et al. A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts , 2004, ACL.
[48] Serkan Günal,et al. The impact of preprocessing on text classification , 2014, Inf. Process. Manag..
[49] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[50] Ting Liu,et al. Document Modeling with Gated Recurrent Neural Network for Sentiment Classification , 2015, EMNLP.
[51] Bo Pang,et al. Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales , 2005, ACL.
[52] Nigel Collier,et al. Sentiment Analysis using Support Vector Machines with Diverse Information Sources , 2004, EMNLP.
[53] Kyunghyun Cho,et al. Efficient Character-level Document Classification by Combining Convolution and Recurrent Layers , 2016, ArXiv.