网络文本情感计算

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基础版

A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts

高级版

论文列表：

（1）Convolutional Neural Networks for Sentence Classification.

（2）Hierarchical Attention Networks for Document Classification.

（3）《Efficient Estimation of Word Representations in Vector Space》(original word2vec paper)

（4）《GloVe: Global Vectors for Word Representation》（original GloVe paper）

（5）《Sequence Modeling: Recurrent and Recursive Neural Nets》(Sections 10.1 and 10.2)

（6）《Learning long-term dependencies with gradient descent is difficult》(one of the original vanishing gradient papers)

（7）《Neural Machine Translation by Jointly Learning to Align and Translate》(original seq2seq+attention paper)

（8）《Practical Methodology》(Deep Learning book chapter)

（9）《Attention Is All You Need》

（10）《BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding》

（11）《Reading Wikipedia to Answer Open-Domain Questions》

（12）《Get To The Point: Summarization with Pointer-Generator Networks》

（13）《End-to-end Neural Coreference Resolution.》

（14）《Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer》

（15）《Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model》

实体识别：实体识别是将文本中的实体与知识图谱中的实体进行匹配的过程。常用的实体识别算法有命名实体识别(Named Entity Recognition，NER)和实体链接(Entity Linking，EL)。

关系抽取：关系抽取是从文本中抽取实体之间的关系的过程。常用的关系抽取算法有规则基于的方法(Rule-based Method)和机器学习基于的方法(Machine Learning Based Method)。

情感分析：情感分析是将文本中的情感倾向与实体进行关联的过程。常用的情感分析算法有基于词汇量的方法(Lexicon-based Method)和深度学习基于的方法(Deep Learning Based Method)。

原文链接：https://blog.csdn.net/universsky2015/article/details/135787422

Wenxuan Wang