【嵌入模型】真棒的嵌入模型

A curated list of awesome embedding models tutorials, projects and communities. Please feel free to pull requests to add links.

Table of Contents

• Papers
• Researchers
• Courses and Lectures
• Datasets
• Implementations and Tools

Papers

Word Embeddings

Word2vec, GloVe, FastText

• Efficient Estimation of Word Representations in Vector Space (2013), T. Mikolov et al. [pdf]
• Distributed Representations of Words and Phrases and their Compositionality (2013), T. Mikolov et al. [pdf]
• word2vec Parameter Learning Explained (2014), Xin Rong [pdf]
• word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method (2014), Yoav Goldberg, Omer Levy [pdf]
• GloVe: Global Vectors for Word Representation (2014), J. Pennington et al. [pdf]
• Improving Word Representations via Global Context and Multiple Word Prototypes (2012), EH Huang et al. [pdf]
• Enriching Word Vectors with Subword Information (2016), P. Bojanowski et al. [pdf]
• Bag of Tricks for Efficient Text Classification (2016), A. Joulin et al. [pdf]

Language Model

• Semi-supervised sequence tagging with bidirectional language models (2017), Peters, Matthew E., et al. [pdf]
• Deep contextualized word representations (2018), Peters, Matthew E., et al. [pdf]
• Contextual String Embeddings for Sequence Labeling (2018), Akbik, Alan, Duncan Blythe, and Roland Vollgraf. [pdf]
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018), [pdf]

Embedding Enhancement

• Sentence Embedding:Learning Semantic Sentence Embeddings using Pair-wise Discriminator(2018),Patro et al.[Project Page] [Paper]
• Retrofitting Word Vectors to Semantic Lexicons (2014), M. Faruqui et al. [pdf]
• Better Word Representations with Recursive Neural Networks for Morphology (2013), T.Luong et al. [pdf]
• Dependency-Based Word Embeddings (2014), Omer Levy, Yoav Goldberg [pdf]
• Not All Neural Embeddings are Born Equal (2014), F. Hill et al. [pdf]
• Two/Too Simple Adaptations of Word2Vec for Syntax Problems (2015), W. Ling[pdf]

Comparing count-based vs predict-based method

• Linguistic Regularities in Sparse and Explicit Word Representations (2014), Omer Levy, Yoav Goldberg[pdf]
• Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors (2014), M. Baroni [pdf]
• Improving Distributional Similarity with Lessons Learned from Word Embeddings (2015), Omer Levy [pdf]

Evaluation, Analysis

• Evaluation methods for unsupervised word embeddings (2015), T. Schnabel [pdf]
• Intrinsic Evaluation of Word Vectors Fails to Predict Extrinsic Performance (2016), B. Chiu [pdf]
• Problems With Evaluation of Word Embeddings Using Word Similarity Tasks (2016), M. Faruqui [pdf]
• Improving Reliability of Word Similarity Evaluation by Redesigning Annotation Task and Performance Measure (2016), Oded Avraham, Yoav Goldberg [pdf]
• Evaluating Word Embeddings Using a Representative Suite of Practical Tasks (2016), N. Nayak [pdf]

Phrase, Sentence and Document Embeddings

Sentence

• Skip-Thought Vectors
• A Simple but Tough-to-Beat Baseline for Sentence Embeddings
• An efficient framework for learning sentence representations
• Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning
• Universal Sentence Encoder

Document

• Distributed Representations of Sentences and Documents

Sense Embeddings

• SENSEMBED: Learning Sense Embeddings for Word and Relational Similarity
• Multi-Prototype Vector-Space Models of Word Meaning

Neural Language Models

• Recurrent neural network based language model
• A Neural Probabilistic Language Model
• Linguistic Regularities in Continuous Space Word Representations

Researchers

• Tomas Mikolov
• Yoshua Bengio
• Yoav Goldberg
• Omer Levy
• Kai Chen

Courses and Lectures

• CS224d: Deep Learning for Natural Language Processing
• Udacity Deep Learning

Datasets

Training

• Wikipedia
• WestburyLab.wikicorp.201004

Evaluation

• SemEval-2012 Task 2
• WordSimilarity-353
• Stanford's Contextual Word Similarities (SCWS)
• Stanford Rare Word (RW) Similarity Dataset

Pre-Trained Language Models

Below is pre-trained ELMo models. Adding ELMo to existing NLP systems significantly improves the state-of-the-art for every considered task.

• ELMo by AllenNLP
• ELMo by TensorFlow Hub

Below is pre-trained sent2vec models.

• BioSentVec: sent2vec pretrained vector for biomedical text

Pre-Trained Word Vectors

Convenient downloader for pre-trained word vectors:

• chakin

Links for pre-trained word vectors:

• Word2vec pretrained vector(English Only)
• Word2vec pretrained vectors for 30+ languages
• FastText pretrained vectors for 157 languages
• FastText pretrained vector for Japanese with NEologd
• word vectors trained by GloVe
• Dependency-Based Word Embeddings
• Meta-Embeddings
• Lex-Vec
• Huang et al. (2012)'s embeddings (HSMN+csmRNN)
• Collobert et al. (2011)'s embeddings (CW+csmRNN)
• BPEmb: subword embeddings for 275 languages
• Wikipedia2Vec: pretrained word and entity embeddings for 12 languages
• word2vec-slim
• BioWordVec: fastText pretrained vector for biomedical text

Implementations and Tools

Word2vec

• Original
• gensim
• TensorFlow

GloVe

• Original
• GloVe as an optimized TensorFlow GPU Layer using chakin

原文:https://github.com/Hironsan/awesome-embedding-models