NLP论文:

Distributed Representation

引入distributed representation是为了解决维度爆炸，使用更小的向量空间

一些常见的训练词向量工具：
S-Space(JAVA)
Semanticvectors(JAVA)
Gensim(Python and below)
Pydsm
Dissect
FastText

尤其是CBOW和Skip-gram

CNN

Model:

• Basic CNN
• Window Approach

优势领域：

• Text Classification
• Semantic Matching

RNN(Recurrent)

Models:

• Basic RNN
• LSTM(long short-term Memery)
• GRU(gated recurrent units)

优势领域：

• RNN for word-level classification
• RNN for sentence-level classification
• RNN for generating language: 尤其lstm

注意事项：

• 注意力机制很火Attention Mechanism

Recursive NN

Models:

• basic model

优势领域：

• parsing

DEEP Reinforced and Deep Unsupervisied

A. Reinforcement learning for sequence generation
B. Unsupervised sentence representation learning
C. Deep generative models

MEMORY - AUGMENTED N ETWORKS

记忆增强网络

常见NLP任务

POS(part od speech) Tagging

Syntactic Parse 句法解析

Named-Entity Recognition 命名实体识别

Semantic Role Labeling

语义角色标注 (Semantic Role Labeling, SRL) 是一种浅层的语义分析技术，标注句子中某些短语为给定谓词的论元 (语义角色) ，如施事、受事、时间和地点等。其能够对问答系统、信息抽取和机器翻译等应用产生推动作用。
https://blog.csdn.net/huhehaotechangsha/article/details/80463118

Sentiment Classification

Machine Translation

Dialogue System