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1. Basic Embedding Model（基礎(chǔ)嵌入模型）

1-1. NNLM(Neural Network Language Model)- Predict Next Word

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A Neural Probabilistic LanguageModel(2003)

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1-2. Word2Vec(Skip-gram) - EmbeddingWords and Show Graph

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Distributed Representations of Wordsand Phrases and their Compositionality(2013)

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1-3. FastText(Application Level)- Sentence Classification

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Bag of Tricks for Efficient Text Classification(2016)

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2. CNN(卷積神經(jīng)網(wǎng)絡(luò))

2-1. TextCNN - BinarySentiment Classification

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Convolutional Neural Networks for SentenceClassification(2014)

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2-2. DCNN(Dynamic Convolutional Neural Network)

3. RNN(循環(huán)神經(jīng)網(wǎng)絡(luò))

3-1. TextRNN - Predict NextStep

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Finding Structure in Time(1990)

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3-2. TextLSTM - Autocomplete

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LONG SHORT-TERM MEMORY(1997)

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3-3. Bi-LSTM - Predict NextWord in Long Sentence

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4. Attention Mechanism（注意力機(jī)制）

4-1. Seq2Seq - Change Word

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Learning Phrase Representations using RNN Encoder–Decoder for Statistical MachineTranslation(2014)

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4-2. Seq2Seq with Attention - Translate

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NeuralMachine Translation by Jointly Learning to Align and Translate(2014)

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4-3. Bi-LSTM with Attention - BinarySentiment Classification

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5-1. The Transformer - Translate

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Attention Is All You Need(2017)

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5-2. BERT - ClassificationNext Sentence & Predict Masked Tokens

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BERT: Pre-training of Deep Bidirectional Transformers for LanguageUnderstanding(2018)

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