SlideShare a Scribd company logo

Convolutional neural networks for sentiment classification

Download as PPTX, PDF
Y
Yunchao He

This document discusses various techniques for using convolutional neural networks for sentiment classification. It describes using word embeddings as network parameters that are learned during training or initialized from pre-trained models. It also discusses using sentence matrices and different types of convolutional and pooling layers. Specific CNN models discussed include using different channels, dynamic k-max pooling, semantic clustering, enriching word vectors, and multichannel variable-size convolution. References are provided for several papers on applying CNNs to sentiment classification.

Data & Analytics
Related topics:
Convolutional Neural Networks
1 of 19
Downloaded 66 times
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Convolutional Neural Networks for Sentiment Classification 何云超 yunchaohe@gmail.com
Word Vectors • CNN中使用词向量的三种方法 • 作为网络参数，在模型训练中学习，随机初始化 • 使用词向量模型 (word2vec, GloVe等) 训练词向量，在模型训练中保持不 变 • 使用词向量模型 (word2vec, GloVe等) 训练词向量，用于网络初始化，在 模型训练中调整
Sentence Matrix • 矩阵中的每一行或者每一列为一个词向 量
Convolutional Layer • Wide Convolution • Narrow Convolution The red connections all have the same weight. s+m-1=7-5+1=3 s+m-1=7+5-1=11
Pooling Layer • Max pooling: The idea is to capture the most important feature—one with the highest value—for each feature map.
Dropout: A Simple Way to Prevent Neural Networks from Overfitting • Consider a neural net with one hidden layer. • Each time we present a training example, we randomly omit each hidden unit with probability 0.5. • So we are randomly sampling from 2^H different architectures. • All architectures share weights. • Dropout prevents units from co-adapting (共同作用) too much. H
Dropout: A Simple Way to Prevent Neural Networks from Overfitting
CNN for Sentence Classification [1] • Two channels • CNN-rand • CNN-non-static • CNN-static • CNN-multichannel
DCNN Overview [2] • Convolutional Neural Networks with Dynamic 𝑘- Max Pooling • Wide Convolution • Dynamic 𝑘-Max Pooling • 𝑙：当前卷积层数 • 𝐿：卷积曾总数 • 𝑠：句子长度 • 𝑘 𝑡𝑜𝑝：最高层卷积层参数
• Dynamic 𝑘-Max Pooling • 𝑙：当前卷积层数 • 𝐿：卷积曾总数 • 𝑠：句子长度 • 𝑘 𝑡𝑜𝑝：最高层卷积层参数 • 例 • IF, 𝐿 = 3, 𝑠 = 18 𝑘 𝑡𝑜𝑝 = 3 • Then, 1 3 1 max(3, 18 ) max(3,12)=12 3 k         2 3 2 max(3, 18 ) max(3,6)=6 3 k        
• Folding • 问题： • 卷积操作独立作用于每一行 • 同一行中建立了复杂的依赖 • 全连接层之前，不同行之间相 互独立 • 因此： • Folding操做将每两行相加 • d行降低为d/2 • 每一行都依赖于下层中的两行
Semantic Clustering [3] Sentence Matrix Semantic Candidate Units Semantic Units m=2, 3, …, 句子长度/2 Semantic Cliques
Semantic Clustering Sentence Matrix Semantic Candidate Units Semantic Units m=2, 3, …, 句子长度/2 Semantic Cliques Semantic cliques
seq-CNN [4] • 受启发于图像有RGB、CMYK多通道的思想，将句子视为图像，句 子中的单词视为像素，因此一个d维的词向量可以看成一个有d个 通道的像素 • 例 词汇表 句子 句向量 多通道 . . . [0 0 0] [0 0 0] [1 0 0 ] [0 0 1] [0 1 0]
Enrich word vectors • 使用了字符级的向量 (character-level embeddings)，将词向量和字 符向量的合并在一起作为其向量表示。 [5] • 使用传统的文本特征来扩展词向量，主要包括：大写单词数量、 表情符号、拉长的单词 (Elongated Units)、情感词数量、否定词、 标点符号、clusters、n-grams。[6]
MVCNN: Multichannel Variable-Size Convolution [7] • 不同word embeddings所含有的单 词不一样 • HLBL • Huang • GloVe • SENNA • Word2vec • 对某些unknown words的处理 • Randomly initialized • Projection: (mutual learning) 𝑎𝑟𝑔𝑚𝑖𝑛||𝑤𝑗 − 𝑤𝑗||2
MVCNN:Training • Pretraining • Unsupervised training • Average of context word vectors as a predicted representation of the middle word • To produce good initial values • Training • Logistic regression
References [1] Kim, Y. (n.d.). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). [2] Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (n.d.). A Convolutional Neural Network for Modelling Sentences. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). [3] Wang, P., Xu, J., Xu, B., Liu, C. L., Zhang, H., Wang, F., & Hao, H. (2015). Semantic Clustering and Convolutional Neural Network for Short Text Categorization. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Vol. 2, pp. 352- 357). [4] Johnson, R., & Zhang, T. (n.d.). Effective Use of Word Order for Text Categorization with Convolutional Neural Networks. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. [5] dos Santos, C. N., & Gatti, M. (2014). Deep convolutional neural networks for sentiment analysis of short texts. In Proceedings of the 25th International Conference on Computational Linguistics (COLING), Dublin, Ireland. [6] Tang, D., Wei, F., Qin, B., Liu, T., & Zhou, M. (2014, August). Coooolll: A deep learning system for twitter sentiment classification. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014) (pp. 208-212). [7] Wenpeng Yin, Hinrich Schütze. Multichannel Variable-Size Convolution for Sentence Classification. The 19th SIGNLL Conference on Computational Natural Language Learning (CoNLL'2015, long paper). July 30-31, Peking, China.
谢谢聆听 Q&A 何云超 yunchaohe@gmail.com

More Related Content

PDF
Recurrent Convolutional Neural Networks for Text Classification
Shuangshuang Zhou
 
PPTX
Dcnn for text
捷恩 蔡
 
PDF
(Deep) Neural Networks在 NLP 和 Text Mining 总结
君 廖
 
PPTX
Scalable image recognition model with deep embedding
捷恩 蔡
 
PPTX
Text Mining for Lexicography
Leiden University
 
PDF
Deep Learning for NLP Applications
Samiur Rahman
 
PDF
Deep Learning for NLP: An Introduction to Neural Word Embeddings
Roelof Pieters
 
PDF
Learning to understand phrases by embedding the dictionary
Roelof Pieters
 
Recurrent Convolutional Neural Networks for Text Classification
Shuangshuang Zhou
 
Dcnn for text
捷恩 蔡
 
(Deep) Neural Networks在 NLP 和 Text Mining 总结
君 廖
 
Scalable image recognition model with deep embedding
捷恩 蔡
 
Text Mining for Lexicography
Leiden University
 
Deep Learning for NLP Applications
Samiur Rahman
 
Deep Learning for NLP: An Introduction to Neural Word Embeddings
Roelof Pieters
 
Learning to understand phrases by embedding the dictionary
Roelof Pieters
 

What's hot (20)

PDF
Deep Learning for Natural Language Processing: Word Embeddings
Roelof Pieters
 
PPTX
What is word2vec?
Traian Rebedea
 
PDF
Deep Learning for Information Retrieval
Roelof Pieters
 
PDF
End-to-end Speech Recognition with Recurrent Neural Networks (D3L6 Deep Learn...
Universitat Politècnica de Catalunya
 
PDF
AINL 2016: Filchenkov
Lidia Pivovarova
 
PDF
Deep Learning Architectures for NLP (Hungarian NLP Meetup 2016-09-07)
Márton Miháltz
 
PDF
Deep learning for nlp
Viet-Trung TRAN
 
PDF
Zero shot learning through cross-modal transfer
Roelof Pieters
 
PPTX
Using Text Embeddings for Information Retrieval
Bhaskar Mitra
 
PDF
Anthiil Inside workshop on NLP
Satyam Saxena
 
PDF
BIng NLP Expert - Dl summer-school-2017.-jianfeng-gao.v2
Karthik Murugesan
 
PPTX
Vectorland: Brief Notes from Using Text Embeddings for Search
Bhaskar Mitra
 
PDF
Ted Willke - The Brain’s Guide to Dealing with Context in Language Understanding
MLconf
 
PDF
Deep Learning & NLP: Graphs to the Rescue!
Roelof Pieters
 
PDF
Information Retrieval with Deep Learning
Adam Gibson
 
PDF
Deep Learning for NLP (without Magic) - Richard Socher and Christopher Manning
BigDataCloud
 
PPTX
Deep Learning for Search
Bhaskar Mitra
 
PPTX
A Simple Introduction to Word Embeddings
Bhaskar Mitra
 
PDF
Engineering Intelligent NLP Applications Using Deep Learning – Part 2
Saurabh Kaushik
 
PDF
Visual-Semantic Embeddings: some thoughts on Language
Roelof Pieters
 
Deep Learning for Natural Language Processing: Word Embeddings
Roelof Pieters
 
What is word2vec?
Traian Rebedea
 
Deep Learning for Information Retrieval
Roelof Pieters
 
End-to-end Speech Recognition with Recurrent Neural Networks (D3L6 Deep Learn...
Universitat Politècnica de Catalunya
 
AINL 2016: Filchenkov
Lidia Pivovarova
 
Deep Learning Architectures for NLP (Hungarian NLP Meetup 2016-09-07)
Márton Miháltz
 
Deep learning for nlp
Viet-Trung TRAN
 
Zero shot learning through cross-modal transfer
Roelof Pieters
 
Using Text Embeddings for Information Retrieval
Bhaskar Mitra
 
Anthiil Inside workshop on NLP
Satyam Saxena
 
BIng NLP Expert - Dl summer-school-2017.-jianfeng-gao.v2
Karthik Murugesan
 
Vectorland: Brief Notes from Using Text Embeddings for Search
Bhaskar Mitra
 
Ted Willke - The Brain’s Guide to Dealing with Context in Language Understanding
MLconf
 
Deep Learning & NLP: Graphs to the Rescue!
Roelof Pieters
 
Information Retrieval with Deep Learning
Adam Gibson
 
Deep Learning for NLP (without Magic) - Richard Socher and Christopher Manning
BigDataCloud
 
Deep Learning for Search
Bhaskar Mitra
 
A Simple Introduction to Word Embeddings
Bhaskar Mitra
 
Engineering Intelligent NLP Applications Using Deep Learning – Part 2
Saurabh Kaushik
 
Visual-Semantic Embeddings: some thoughts on Language
Roelof Pieters
 
Ad

Viewers also liked (20)

PDF
Convolutional Neural Networks (CNN)
Gaurav Mittal
 
PDF
Deep Convolutional Neural Networks - Overview
Keunwoo Choi
 
PDF
101: Convolutional Neural Networks
Mad Scientists
 
PPTX
CNN for Text Classification
Emory NLP
 
PPTX
Lecture 29 Convolutional Neural Networks - Computer Vision Spring2015
Jia-Bin Huang
 
PPTX
Deep Learning - Convolutional Neural Networks - Architectural Zoo
Christian Perone
 
PDF
connected_issue_49_summer_2013
Mary Stephanou
 
PPT
SNLI_presentation_2
Viral Gupta
 
PDF
ujava.org Deep Learning with Convolutional Neural Network
신동 강
 
PPTX
Yunchao he icot2015
Yunchao He
 
PDF
Understanding Convolutional Neural Networks
Jeremy Nixon
 
PDF
"Source Code Abstracts Classification Using CNN", Vadim Markovtsev, Lead Soft...
Dataconomy Media
 
PDF
Learning Convolutional Neural Networks for Graphs
Mathias Niepert
 
PPTX
Jeff Johnson, Research Engineer, Facebook at MLconf NYC
MLconf
 
PDF
Scene classification using Convolutional Neural Networks - Jayani Withanawasam
WithTheBest
 
PDF
Lukáš Vrábel - Deep Convolutional Neural Networks
Machine Learning Prague
 
PDF
Deep learning for music classification, 2016-05-24
Keunwoo Choi
 
PDF
Automatic Tagging using Deep Convolutional Neural Networks - ISMIR 2016
Keunwoo Choi
 
PPTX
Introduction to CNN
Shuai Zhang
 
PDF
CNNs: from the Basics to Recent Advances
Dmytro Mishkin
 
Convolutional Neural Networks (CNN)
Gaurav Mittal
 
Deep Convolutional Neural Networks - Overview
Keunwoo Choi
 
101: Convolutional Neural Networks
Mad Scientists
 
CNN for Text Classification
Emory NLP
 
Lecture 29 Convolutional Neural Networks - Computer Vision Spring2015
Jia-Bin Huang
 
Deep Learning - Convolutional Neural Networks - Architectural Zoo
Christian Perone
 
connected_issue_49_summer_2013
Mary Stephanou
 
SNLI_presentation_2
Viral Gupta
 
ujava.org Deep Learning with Convolutional Neural Network
신동 강
 
Yunchao he icot2015
Yunchao He
 
Understanding Convolutional Neural Networks
Jeremy Nixon
 
"Source Code Abstracts Classification Using CNN", Vadim Markovtsev, Lead Soft...
Dataconomy Media
 
Learning Convolutional Neural Networks for Graphs
Mathias Niepert
 
Jeff Johnson, Research Engineer, Facebook at MLconf NYC
MLconf
 
Scene classification using Convolutional Neural Networks - Jayani Withanawasam
WithTheBest
 
Lukáš Vrábel - Deep Convolutional Neural Networks
Machine Learning Prague
 
Deep learning for music classification, 2016-05-24
Keunwoo Choi
 
Automatic Tagging using Deep Convolutional Neural Networks - ISMIR 2016
Keunwoo Choi
 
Introduction to CNN
Shuai Zhang
 
CNNs: from the Basics to Recent Advances
Dmytro Mishkin
 
Ad

Similar to Convolutional neural networks for sentiment classification (20)

PPTX
Deep Neural Methods for Retrieval
Bhaskar Mitra
 
PDF
AINL 2016: Nikolenko
Lidia Pivovarova
 
PPTX
Advanced_NLP_with_Transformers_PPT_final 50.pptx
Shiwani Gupta
 
PDF
IRJET- Survey on Deep Learning Approaches for Phrase Structure Identification...
IRJET Journal
 
PPTX
Word embedding
ShivaniChoudhary74
 
PPTX
Natural Language Processing Advancements By Deep Learning: A Survey
Rimzim Thube
 
PDF
Phrase Structure Identification and Classification of Sentences using Deep Le...
ijtsrd
 
PDF
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
kevig
 
PDF
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
kevig
 
PDF
Survey on Text Prediction Techniques
vivatechijri
 
PPTX
wordembedding.pptx
JOBANPREETSINGH62
 
PPTX
NLP Introduction and basics of natural language processing
mailtoahmedhassan
 
PDF
Suggestion Generation for Specific Erroneous Part in a Sentence using Deep Le...
ijtsrd
 
PDF
LSTM Based Sentiment Analysis
ijtsrd
 
PDF
mlss
MaiAGE-INRA, Paris Sud, LIMSI-CNRS
 
PDF
A survey on phrase structure learning methods for text classification
ijnlc
 
PDF
is2015_poster
Jan Svec
 
PPTX
Talk from NVidia Developer Connect
Anuj Gupta
 
ODP
Tensorflow
Knoldus Inc.
 
PPTX
Networking lesson 4 chaoter 1 Module 4-1.pptx
MAHERMOHAMED27
 
Deep Neural Methods for Retrieval
Bhaskar Mitra
 
AINL 2016: Nikolenko
Lidia Pivovarova
 
Advanced_NLP_with_Transformers_PPT_final 50.pptx
Shiwani Gupta
 
IRJET- Survey on Deep Learning Approaches for Phrase Structure Identification...
IRJET Journal
 
Word embedding
ShivaniChoudhary74
 
Natural Language Processing Advancements By Deep Learning: A Survey
Rimzim Thube
 
Phrase Structure Identification and Classification of Sentences using Deep Le...
ijtsrd
 
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
kevig
 
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
kevig
 
Survey on Text Prediction Techniques
vivatechijri
 
wordembedding.pptx
JOBANPREETSINGH62
 
NLP Introduction and basics of natural language processing
mailtoahmedhassan
 
Suggestion Generation for Specific Erroneous Part in a Sentence using Deep Le...
ijtsrd
 
LSTM Based Sentiment Analysis
ijtsrd
 
mlss
MaiAGE-INRA, Paris Sud, LIMSI-CNRS
 
A survey on phrase structure learning methods for text classification
ijnlc
 
is2015_poster
Jan Svec
 
Talk from NVidia Developer Connect
Anuj Gupta
 
Tensorflow
Knoldus Inc.
 
Networking lesson 4 chaoter 1 Module 4-1.pptx
MAHERMOHAMED27
 

Recently uploaded (20)

PPTX
Computer network topology notes for revision
BatoolRawat
 
PPTX
Introduction to Firewall Analytics - Interfirewall and Transfirewall.pptx
kuthubussaman1
 
PDF
Introduction to Data Science and Data Analysis
Suraj Patil
 
PPT
Miokarditis (Inflamasi pada Otot Jantung)
NandaAndini1
 
PPTX
STERILIZATION AND DISINFECTION-1.ppthhhbx
DivuuJain
 
PPTX
The THESIS FINAL-DEFENSE-PRESENTATION.pptx
hshakobe3
 
PPTX
IBA_Chapter_11_Slides_Final_Accessible.pptx
SrikantKapoor1
 
PPTX
Introduction to Basics of Ethical Hacking and Penetration Testing -Unit No. 1...
AshutoshDeshmukh33
 
PDF
Fluorescence-microscope_Botany_detailed content
dollydoll12
 
PDF
Mega Projects Data Mega Projects Data
saidsalah8181
 
PDF
22.Patil - Early prediction of Alzheimer’s disease using convolutional neural...
ngaviet5
 
PDF
Business Analytics and business intelligence.pdf
khalidisah4750
 
PPTX
STUDY DESIGN details- Lt Col Maksud (21).pptx
AyeshaAsha11
 
PPTX
DISORDERS OF THE LIVER, GALLBLADDER AND PANCREASE (1).pptx
chepkoitcheruiyot
 
PPT
ISS -ESG Data flows What is ESG and HowHow
lucandthemachine
 
PDF
168300704-gasification-ppt.pdfhghhhsjsjhsuxush
sriram270905
 
PPT
Reliability_Chapter_ presentation 1221.5784
abobaker13
 
PPTX
Acceptance and paychological effects of mandatory extra coach I classes.pptx
ZaheerAhmad228692
 
PDF
annual-report-2024-2025 original latest.
nityanema19
 
PPTX
Introduction-to-Cloud-ComputingFinal.pptx
testnet29393883
 
Computer network topology notes for revision
BatoolRawat
 
Introduction to Firewall Analytics - Interfirewall and Transfirewall.pptx
kuthubussaman1
 
Introduction to Data Science and Data Analysis
Suraj Patil
 
Miokarditis (Inflamasi pada Otot Jantung)
NandaAndini1
 
STERILIZATION AND DISINFECTION-1.ppthhhbx
DivuuJain
 
The THESIS FINAL-DEFENSE-PRESENTATION.pptx
hshakobe3
 
IBA_Chapter_11_Slides_Final_Accessible.pptx
SrikantKapoor1
 
Introduction to Basics of Ethical Hacking and Penetration Testing -Unit No. 1...
AshutoshDeshmukh33
 
Fluorescence-microscope_Botany_detailed content
dollydoll12
 
Mega Projects Data Mega Projects Data
saidsalah8181
 
22.Patil - Early prediction of Alzheimer’s disease using convolutional neural...
ngaviet5
 
Business Analytics and business intelligence.pdf
khalidisah4750
 
STUDY DESIGN details- Lt Col Maksud (21).pptx
AyeshaAsha11
 
DISORDERS OF THE LIVER, GALLBLADDER AND PANCREASE (1).pptx
chepkoitcheruiyot
 
ISS -ESG Data flows What is ESG and HowHow
lucandthemachine
 
168300704-gasification-ppt.pdfhghhhsjsjhsuxush
sriram270905
 
Reliability_Chapter_ presentation 1221.5784
abobaker13
 
Acceptance and paychological effects of mandatory extra coach I classes.pptx
ZaheerAhmad228692
 
annual-report-2024-2025 original latest.
nityanema19
 
Introduction-to-Cloud-ComputingFinal.pptx
testnet29393883
 

Convolutional neural networks for sentiment classification

  • 1. Convolutional Neural Networks for Sentiment Classification 何云超 yunchaohe@gmail.com
  • 2. Word Vectors • CNN中使用词向量的三种方法 • 作为网络参数，在模型训练中学习，随机初始化 • 使用词向量模型 (word2vec, GloVe等) 训练词向量，在模型训练中保持不 变 • 使用词向量模型 (word2vec, GloVe等) 训练词向量，用于网络初始化，在 模型训练中调整
  • 4. Convolutional Layer • Wide Convolution • Narrow Convolution The red connections all have the same weight. s+m-1=7-5+1=3 s+m-1=7+5-1=11
  • 5. Pooling Layer • Max pooling: The idea is to capture the most important feature—one with the highest value—for each feature map.
  • 6. Dropout: A Simple Way to Prevent Neural Networks from Overfitting • Consider a neural net with one hidden layer. • Each time we present a training example, we randomly omit each hidden unit with probability 0.5. • So we are randomly sampling from 2^H different architectures. • All architectures share weights. • Dropout prevents units from co-adapting (共同作用) too much. H
  • 7. Dropout: A Simple Way to Prevent Neural Networks from Overfitting
  • 8. CNN for Sentence Classification [1] • Two channels • CNN-rand • CNN-non-static • CNN-static • CNN-multichannel
  • 9. DCNN Overview [2] • Convolutional Neural Networks with Dynamic 𝑘- Max Pooling • Wide Convolution • Dynamic 𝑘-Max Pooling • 𝑙：当前卷积层数 • 𝐿：卷积曾总数 • 𝑠：句子长度 • 𝑘 𝑡𝑜𝑝：最高层卷积层参数
  • 10. • Dynamic 𝑘-Max Pooling • 𝑙：当前卷积层数 • 𝐿：卷积曾总数 • 𝑠：句子长度 • 𝑘 𝑡𝑜𝑝：最高层卷积层参数 • 例 • IF, 𝐿 = 3, 𝑠 = 18 𝑘 𝑡𝑜𝑝 = 3 • Then, 1 3 1 max(3, 18 ) max(3,12)=12 3 k         2 3 2 max(3, 18 ) max(3,6)=6 3 k        
  • 11. • Folding • 问题： • 卷积操作独立作用于每一行 • 同一行中建立了复杂的依赖 • 全连接层之前，不同行之间相 互独立 • 因此： • Folding操做将每两行相加 • d行降低为d/2 • 每一行都依赖于下层中的两行
  • 13. Semantic Clustering Sentence Matrix Semantic Candidate Units Semantic Units m=2, 3, …, 句子长度/2 Semantic Cliques Semantic cliques
  • 15. Enrich word vectors • 使用了字符级的向量 (character-level embeddings)，将词向量和字 符向量的合并在一起作为其向量表示。 [5] • 使用传统的文本特征来扩展词向量，主要包括：大写单词数量、 表情符号、拉长的单词 (Elongated Units)、情感词数量、否定词、 标点符号、clusters、n-grams。[6]
  • 16. MVCNN: Multichannel Variable-Size Convolution [7] • 不同word embeddings所含有的单 词不一样 • HLBL • Huang • GloVe • SENNA • Word2vec • 对某些unknown words的处理 • Randomly initialized • Projection: (mutual learning) 𝑎𝑟𝑔𝑚𝑖𝑛||𝑤𝑗 − 𝑤𝑗||2
  • 17. MVCNN:Training • Pretraining • Unsupervised training • Average of context word vectors as a predicted representation of the middle word • To produce good initial values • Training • Logistic regression
  • 18. References [1] Kim, Y. (n.d.). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). [2] Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (n.d.). A Convolutional Neural Network for Modelling Sentences. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). [3] Wang, P., Xu, J., Xu, B., Liu, C. L., Zhang, H., Wang, F., & Hao, H. (2015). Semantic Clustering and Convolutional Neural Network for Short Text Categorization. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Vol. 2, pp. 352- 357). [4] Johnson, R., & Zhang, T. (n.d.). Effective Use of Word Order for Text Categorization with Convolutional Neural Networks. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. [5] dos Santos, C. N., & Gatti, M. (2014). Deep convolutional neural networks for sentiment analysis of short texts. In Proceedings of the 25th International Conference on Computational Linguistics (COLING), Dublin, Ireland. [6] Tang, D., Wei, F., Qin, B., Liu, T., & Zhou, M. (2014, August). Coooolll: A deep learning system for twitter sentiment classification. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014) (pp. 208-212). [7] Wenpeng Yin, Hinrich Schütze. Multichannel Variable-Size Convolution for Sentence Classification. The 19th SIGNLL Conference on Computational Natural Language Learning (CoNLL'2015, long paper). July 30-31, Peking, China.