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Benmark:
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ACL anthology for NLP papers:
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Online proceedings of major ML conferences:
- NeurIPS
- ICML, ICLR, CVPR, EMNLP, NAACL
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Online preprint servers:
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Top paper menioned on Twitter:
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Others:
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Huggingface Datasets:
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Kaggle has many datasets, though some of them are too small for Deep Learning:
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SOTA NLP:
https://paperswithcode.com/sota
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A small list of well-known standard datasets for common NLP tasks: https://machinelearningmastery.com/datasets-natural-language-processing/
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An alphabetical list of free or public domain text datasets:
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Wikipedia has a list of machine learning text datasets, tabulated with useful information such as dataset size: https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research#Text_data
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Datahub has lots of datasets, though not all of it is Machine Learning focused:
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Microsoft Research has a collection of datasets (look under the ‘Dataset directory’ tab): https://www.microsoft.com/en-us/research/academic-program/data-science-microsoft-research/?from=http%3A%2F%2Fresearch.microsoft.com%2Fen-us%2Fprojects%2Fdata-science-initiative%2F%20datasets.aspx#!dataset-directory
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A script to search arXiv papers for a keyword, and extract important information such as performance metrics on a task:
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Datasets for machine translation:
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Syntactic corpora for many languages:
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StanfordNLP: a Python library providing tokenization, tagging, parsing, and other capabilities:
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Other software from the Stanford NLP group: http://nlp.stanford.edu/software/index.shtml
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NLTK, a lightweight Natural Language Toolkit package in Python: http://nltk.org/
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spaCy, another Python package that can do preprocessing, but also includes neural models (e.g. Language Models):
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Site
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NeurIPS: Neural Information Processing Systems (formerly abbreviated NIPS). NeurIPS has gotten huge over the past few years as AI has become so important. Has a focus on neural networks, but not exclusively.
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ICML: International Conference on Machine Learning. Has a general machine learning focus.
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ICLR: International Conference on Learning Representations. ICLR was really the first conference focused on deep learning. It’s called “learning representations” because the motivation behind deep learning is to automatically learn higher-level features, or representations, that summarize data in useful ways. Deep Learning describes the structure of our current best solution to the problem of learning these representations.
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AAAI: Association for the Advancement of Artificial Intelligence. AAAI is a little more applications focused, and a little less theoretical than some of the other AI conferences.
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CVPR: Computer Vision and Pattern Recognition.
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ICCV: International Conference on Computer Vision.