Deep Learning for NLP 文章列举 | 持之以恒-优快云博客

本文综述了深度学习在自然语言处理领域的应用，涵盖了词嵌入学习、语义提取、文档表示、情感分析等关键技术。引用了多篇重要论文，如Word Representations、Deep Neural Network with Multitask Learning等，探讨了神经网络架构、多任务学习、语义解析等前沿研究。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

慢慢补充

大部分文章来自:

http://www.socher.org/

http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial

包括从他们里面的论文里找到的related work

Word Embedding Learnig

SENNA原始论文【ACL'07】Fast Semantic Extraction Using a Novel Neural Network Architecture

Ronan Collobert and Jason Weston【ICML'08】A unified architecture for natural language processing: deep neural networks with multitask learning

Joseph Turian, et al.【ACL'10】Word representations：A simple and general method for semi-supervised learning

Antoine Bordes, et al. 【AAAI'11】Learning Structured Embeddings of Knowledge Bases

our model learns one embedding for each entity (i.e. one low dimensional vector) and one operator for each relation (i.e. a matrix).

Ronan Collobert, et al.【JMLR'12】Natural Language Processing (Almost) from Scratch

Eric H. Huang, et al.【ACL'12】Improving Word Representations via Global Context and Multiple Word Prototypes

T. Mikolov, et al.【HLT-NAACL'13】Linguistic regularities in continuous spaceword representations

Yoshua Bengio et al,【13】 Representation Learning: A Review and New Perspectives

待读列表：

Semi-supervised learning of compact document representations with deep networks

【UAI'13】Modeling Documents with a Deep Boltzmann Machine

Language Model

Y. Bengio, et al. Neural probabilistic language model

博士论文：Statistical Language Models based on Neural Networks 这人貌似在ICASSP上有个文章

T Mikolov Statistical Language Models Based on Neural Networks

Sentiment

【HLT'11】Learning word vectors for sentiment analysis

【EMNLP'11】Semi-supervised recursive autoencoders for predicting sentiment distributions

【NAACL'13】 Discourse Connectors for Latent Subjectivity in Sentiment Analysis

other NLP 以下内容见socher主页

Parsing with Compositional Vector Grammars

Better Word Representations with Recursive Neural Networks for Morphology
Semantic Compositionality through Recursive Matrix-Vector Spaces
Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection
Parsing Natural Scenes and Natural Language with Recursive Neural Networks
Learning Continuous Phrase Representations and Syntactic Parsing with Recursive Neural Networks

Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing

Tutorials

Ronan Collobert and Jason Weston【NIPS'09】Deep Learning for Natural Language Processing

Richard Socher, et al.【NAACL'13】【ACL'12】Deep Learning for NLP

Yoshua Bengio【ICML'12】Representation Learning

L eon Bottou, Natural language processing and weak supervision

Yoshua Bengio最新AAAI 2013 tutorial：http://www.iro.umontreal.ca/~bengioy/talks/aaai2013-tutorial.pdf

Socher NAACL 2013：http://nlp.stanford.edu/courses/NAACL2013/

原文地址：http://www.xperseverance.net/blogs/2013/07/2124/

转载于:https://my.oschina.net/CaptainA/blog/1483785