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https://github.com/JackHCC/NLP-Bubble
🖨 Natural Language Processing Learning Blog，a Study Bubble to recording learning.
https://github.com/JackHCC/NLP-Bubble
List: NLP-Bubble
awesome machine-learning nlp
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🖨 Natural Language Processing Learning Blog，a Study Bubble to recording learning.
- Host: GitHub
- URL: https://github.com/JackHCC/NLP-Bubble
- Owner: JackHCC
- License: mit
- Created: 2022-01-24T10:46:05.000Z (almost 3 years ago)
- Default Branch: master
- Last Pushed: 2022-07-01T13:02:34.000Z (over 2 years ago)
- Last Synced: 2024-11-19T01:02:16.226Z (about 1 month ago)
- Topics: awesome, machine-learning, nlp
- Homepage:
- Size: 2.51 MB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- ultimate-awesome - NLP-Bubble - 🖨 Natural Language Processing Learning Blog，a Study Bubble to recording learning. (Other Lists / Monkey C Lists)
README
# NLP-Bubble
🖨 Natural Language Processing Learning Blog，a Study Bubble to recording learning.
💡 NLP Learning Record 💡
## Lessons/Books
- [Statistical Learning Method v1](https://blog.creativecc.cn/posts/Lesson-Statistical-Learning-Method.html)
- [CS224N Natural Language Processing 2022](https://github.com/JackHCC/Awesome-DL-Models/tree/master/Docx/CS224N)
- [CS224W Machine Learning with Graphs 2021](https://blog.creativecc.cn/posts/Lesson-CS224W-Machine-Learning-with-Graphs.html)
## Papers
- [Arxiv NLP Reporter](https://github.com/JackHCC/Arxiv-NLP-Reporter)
- [Web Reader](https://blog.creativecc.cn/Arxiv-NLP-Reporter/)
- Reading Web
- [ACL anthology](https://www.aclweb.org/anthology/)
- [NeurIPS](https://papers.nips.cc) , ICML, ICLR
- [online preprint servers](https://arxiv.org)
## DataSet
### Classes
- Linguistic Data Consortium
- [Linguistic Data Consortium (upenn.edu)](https://catalog.ldc.upenn.edu/)
- [Linguistics (stanford.edu)](https://linguistics.stanford.edu/resources/resources-corpora)
- Machine translation
- [Statistical Machine Translation (statmt.org)](https://statmt.org/)
- Dependency parsing: Universal Dependencies
- [Universal Dependencies](https://universaldependencies.org/)
### Other
- Awesome
- [NLPDataSet](https://github.com/liucongg/NLPDataSet)
- [nlp-datasets](https://github.com/niderhoff/nlp-datasets)
- Platform
- [千言：中文开源数据集合](https://www.luge.ai/)
- [Papers With Code](https://paperswithcode.com/datasets)
- Kaggle
- [GLUE](https://gluebenchmark.com/tasks)
- Blogs
- [Datasets for Natural Language Processing](https://machinelearningmastery.com/datasets-natural-language-processing/)
- [Sentiment Analysis](https://nlp.stanford.edu/sentiment/)
- [The bAbI](https://research.facebook.com/downloads/babi/)
## NLP Task
思维导图：
常见的32项NLP任务以及对应的评测数据、评测指标、目前的SOTA结果（2020.05）以及对应的Paper与Code.
| 任务 | 描述 | corpus/dataset | 评价指标 | SOTA | Papers | Code |
| ---------------------------------------------- | ------------------- | ------------------------------------ | ------------------------------------------ | ------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| Chunking | 组块分析 | Penn Treebank | F1 | 95.77 | [A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks](https://arxiv.org/pdf/1611.01587v5.pdf) | [Link](https://github.com/hassyGo/charNgram2vec) |
| Common sense reasoning | 常识推理 | Event2Mind | cross-entropy | 4.22 | [Event2Mind: Commonsense Inference on Events, Intents, and Reactions](https://www.dialog-21.ru/media/5090/fenogenovaasplusetal-010.pdf) | [Link](https://github.com/Alenush/russian_event2mind) |
| Parsing | 句法分析 | Penn Treebank | F1 | 95.13 | [Constituency Parsing with a Self-Attentive Encoder](https://arxiv.org/pdf/1805.01052v1.pdf) | [Link](https://github.com/nikitakit/self-attentive-parser) |
| Coreference resolution | 指代消解 | CoNLL 2012 | average F1 | 73 | [Higher-order Coreference Resolution with Coarse-to-fine Inference](https://arxiv.org/pdf/1804.05392v1.pdf) | [Link](https://github.com/kentonl/e2e-coref) |
| Dependency parsing | 依存句法分析 | Penn Treebank | POS
UAS
LAS | 97.3
95.44
93.76 | [Deep Biaffine Attention for Neural Dependency Parsing](https://arxiv.org/pdf/1611.01734v3.pdf) | [Link](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/dependency_parsing/ddparser) |
| Task-Oriented Dialogue/Intent Detection | 任务型对话/意图识别 | ATIS/Snips | accuracy | 94.1 97.0 | [Slot-Gated Modeling for Joint Slot Filling and Intent Prediction](https://aclanthology.org/N18-2118.pdf) | [Link](https://github.com/MiuLab/SlotGated-SLU) |
| Task-Oriented Dialogue/Slot Filling | 任务型对话/槽填充 | ATIS/Snips | F1 | 95.2
88.8 | [Slot-Gated Modeling for Joint Slot Filling and Intent Prediction](https://aclanthology.org/N18-2118.pdf) | [Link](https://github.com/MiuLab/SlotGated-SLU) |
| Task-Oriented Dialogue/Dialogue State Tracking | 任务型对话/状态追踪 | DSTC2 | Area
Food
Price
Joint | 90
84
92
72 | [Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems](https://arxiv.org/pdf/1804.06512v1.pdf) | [Link](https://github.com/google-research-datasets/simulated-dialogue) |
| Domain adaptation | 领域适配 | Multi-Domain Sentiment Dataset | average
accuracy | 79.15 | [Strong Baselines for Neural Semi-supervised Learning under Domain Shift](https://arxiv.org/pdf/1804.09530v1.pdf) | [Link](https://github.com/bplank/semi-supervised-baselines) |
| Entity Linking | 实体链接 | AIDA CoNLL-YAGO | Micro-F1-strong
Macro-F1-strong | 86.6
89.4 | [End-to-End Neural Entity Linking](https://arxiv.org/pdf/1808.07699v2.pdf) | [Link](https://github.com/dalab/end2end_neural_el) |
| Information Extraction | 信息抽取 | ReVerb45K | Precision
Recall
F1 | 62.7
84.4
81.9 | [CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information](https://arxiv.org/pdf/1902.00172v1.pdf) | [Link](https://github.com/malllabiisc/cesi) |
| Grammatical Error Correction | 语法错误纠正 | JFLEG | GLEU | 61.5 | [Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation](https://arxiv.org/pdf/1804.05945v1.pdf) | Link |
| Language modeling | 语言模型 | Penn Treebank | Validation perplexity
Test perplexity | 48.33
47.69 | [Breaking the Softmax Bottleneck: A High-Rank RNN Language Model](https://arxiv.org/pdf/1711.03953v4.pdf) | [Link](https://github.com/zihangdai/mos) |
| Lexical Normalization | 词汇规范化 | LexNorm2015 | F1
Precision
Recall | 86.39
93.53
80.26 | [MoNoise: Modeling Noise Using a Modular Normalization System](https://arxiv.org/pdf/1710.03476v1.pdf) | [Link](https://bitbucket.org/robvanderg/monoise) |
| Machine translation | 机器翻译 | WMT 2014 EN-DE | BLEU | 35.0 | [Understanding Back-Translation at Scale](https://arxiv.org/pdf/1808.09381v2.pdf) | [Link](https://github.com/pytorch/fairseq) |
| Multimodal Emotion Recognition | 多模态情感识别 | IEMOCAP | Accuracy | 76.5 | [Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling](https://arxiv.org/pdf/1806.06228v1.pdf) | [Link](https://github.com/SenticNet/hfusion) |
| Multimodal Metaphor Recognition | 多模态隐喻识别 | verb-noun pairs adjective-noun pairs | F1 | 0.75
0.79 | [Black Holes and White Rabbits: Metaphor Identification with Visual Features](https://aclanthology.org/N16-1020.pdf) | Link |
| Multimodal Sentiment Analysis | 多模态情感分析 | MOSI | Accuracy | 80.3 | [Context-Dependent Sentiment Analysis in User-Generated Videos](https://aclanthology.org/P17-1081.pdf) | [Link](https://github.com/senticnet/sc-lstm) |
| Named entity recognition | 命名实体识别 | CoNLL 2003 | F1 | 93.09 | [Contextual String Embeddings for Sequence Labeling](https://aclanthology.org/C18-1139.pdf) | [Link](https://github.com/zalandoresearch/flair) |
| Natural language inference | 自然语言推理 | SciTail | Accuracy | 88.3 | [Improving Language Understanding by Generative Pre-Training](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf) | [Link](https://github.com/huggingface/transformers) |
| Part-of-speech tagging | 词性标注 | Penn Treebank | Accuracy | 97.96 | [Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings](https://arxiv.org/pdf/1805.08237v1.pdf) | [Link](https://github.com/google/meta_tagger) |
| Question answering | 问答 | CliCR | F1 | 33.9 | [CliCR: A Dataset of Clinical Case Reports for Machine Reading Comprehension](https://arxiv.org/pdf/1803.09720v1.pdf) | [Link](https://github.com/clips/clicr) |
| Word segmentation | 分词 | VLSP 2013 | F1 | 97.90 | [A Fast and Accurate Vietnamese Word Segmenter](https://arxiv.org/pdf/1709.06307v2.pdf) | [Link](https://github.com/datquocnguyen/RDRsegmenter) |
| Word Sense Disambiguation | 词义消歧 | SemEval 2015 | F1 | 67.1 | [Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison](https://aclanthology.org/E17-1010.pdf) | Link |
| Text classification | 文本分类 | AG News | Error rate | 5.01 | [Universal Language Model Fine-tuning for Text Classification](https://arxiv.org/pdf/1801.06146v5.pdf) | [Link](https://github.com/fastai/fastai) |
| Summarization | 摘要 | Gigaword | ROUGE-1
ROUGE-2
ROUGE-L | 37.04
19.03
34.46 | [Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization](https://aclanthology.org/P18-1015.pdf) | Link |
| Sentiment analysis | 情感分析 | IMDb | Accuracy | 95.4 | [Universal Language Model Fine-tuning for Text Classification](https://arxiv.org/pdf/1801.06146v5.pdf) | [Link](https://github.com/fastai/fastai) |
| Semantic role labeling | 语义角色标注 | OntoNotes | F1 | 85.5 | [Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling](https://arxiv.org/pdf/1805.04787v2.pdf) | [Link](https://github.com/luheng/lsgn) |
| Semantic parsing | 语义解析 | LDC2014T12 | F1 Newswire
F1 Full | 0.71
0.66 | [AMR Parsing with an Incremental Joint Model](https://arxiv.org/pdf/1909.04303v2.pdf) | [Link](https://github.com/jcyk/AMR-parser) |
| Semantic textual similarity | 语义文本相似度 | SentEval | MRPC
SICK-R
SICK-E
STS | 78.6/84.4
0.888
87.8
78.9/78.6 | [Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning](https://arxiv.org/pdf/1804.00079v1.pdf) | [Link](https://github.com/facebookresearch/SentEval) |
| Relationship Extraction | 关系抽取 | New York Times Corpus | P@10%
P@30% | 73.6
59.5 | [RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information](https://arxiv.org/pdf/1812.04361v2.pdf) | [Link](https://github.com/malllabiisc/RESIDE) |
| Relation Prediction | 关系预测 | WN18RR | H@10
H@1
MRR | 59.02
45.37
49.83 | [Predicting Semantic Relations using Global Graph Properties](https://arxiv.org/pdf/1808.08644v1.pdf) | [Link](https://github.com/yuvalpinter/m3gm) |
## Resource
- [NLP-Interview-Notes](https://github.com/km1994/NLP-Interview-Notes)
- [Recommendation-Advertisement-Search](https://github.com/km1994/recommendation_advertisement_search)
- [NLPer-Arsenal](https://github.com/TingFree/NLPer-Arsenal)
- [AI-Surveys](https://github.com/KaiyuanGao/AI-Surveys)
## Interview
- [Machine Learning](./docs/interview/machine-learning.md)
- [Deep Learning](./docs/interview/deep-learning.md)
- [Word Embedding](./docs/interview/word-embedding.md)
- [Transformer](./docs/interview/transformer.md)
- [Bert](./docs/interview/bert.md)
- [Reverse](./docs/interview/reverse-interview.md)
© [JackHCC](https://github.com/JackHCC)