{ "id": 115874, "title": "More Natural Language Processing Articles", "url": "/competitions/tensorflow2-question-answering/discussion/115874", "author_name": "Gabriel Preda", "post_date": "2019-11-05T19:41:37.572000", "votes": 22, "comment_count": 11, "views": 0, "content": "

I include here few more articles about NLP I found useful:
\n\"\"
\n1. K-Means Clustering with scikit-learn
\n2. Applied Text Analysis with Python, Chapter 4. Text Vectorization and Transformation Pipelines
\n3. ML Data Pipelines with Custom Transformers in Python
\n4. Multi-Class Text Classification with Doc2Vec & Logistic Regression
\n5. Topic Modeling
\n6. Topic Modeling with Gensim (Python)
\n7. Topic Modeling and Latent Dirichlet Allocation (LDA) in Python
\n8. LDA in Python – How to grid search best topic models?
\n9. Named Entity Recognition with NLTK and SpaCy
\n10. I did a Kaggle competition as a semester project at uni. Here’s what I learned.
\n11. @adriensieg/text-similarities-da019229c894\">Text Similarities : Estimate the degree of similarity between two texts
\n12. Multi-Class Text Classification Model Comparison and Selection\n
\n13. Multi-Class Text Classification with Scikit-Learn
\n14. Machine Learning, NLP: Text Classification using scikit-learn, python and NLTK.
\n15. BERT Explained: State of the art language model for NLP

", "messages": [ { "id": 666150, "postDate": "2019-11-05T19:41:37.573Z", "content": "

I include here few more articles about NLP I found useful:
\n\"\"
\n1. K-Means Clustering with scikit-learn
\n2. Applied Text Analysis with Python, Chapter 4. Text Vectorization and Transformation Pipelines
\n3. ML Data Pipelines with Custom Transformers in Python
\n4. Multi-Class Text Classification with Doc2Vec & Logistic Regression
\n5. Topic Modeling
\n6. Topic Modeling with Gensim (Python)
\n7. Topic Modeling and Latent Dirichlet Allocation (LDA) in Python
\n8. LDA in Python – How to grid search best topic models?
\n9. Named Entity Recognition with NLTK and SpaCy
\n10. I did a Kaggle competition as a semester project at uni. Here’s what I learned.
\n11. @adriensieg/text-similarities-da019229c894\">Text Similarities : Estimate the degree of similarity between two texts
\n12. Multi-Class Text Classification Model Comparison and Selection\n
\n13. Multi-Class Text Classification with Scikit-Learn
\n14. Machine Learning, NLP: Text Classification using scikit-learn, python and NLTK.
\n15. BERT Explained: State of the art language model for NLP

", "rawMarkdown": "I include here few more articles about NLP I found useful: \n![](https://algorithmia.com/blog/wp-content/uploads/2016/08/natural-language-processing-introduction.jpg) \n1. [K-Means Clustering with scikit-learn](http://jonathansoma.com/lede/algorithms-2017/classes/clustering/k-means-clustering-with-scikit-learn/) \n2. [Applied Text Analysis with Python, Chapter 4. Text Vectorization and Transformation Pipelines](https://www.oreilly.com/library/view/applied-text-analysis/9781491963036/ch04.html) \n3. [ML Data Pipelines with Custom Transformers in Python](https://towardsdatascience.com/custom-transformers-and-ml-data-pipelines-with-python-20ea2a7adb65) \n4. [Multi-Class Text Classification with Doc2Vec & Logistic Regression](https://towardsdatascience.com/multi-class-text-classification-with-doc2vec-logistic-regression-9da9947b43f4) \n5. [Topic Modeling](https://www.tidytextmining.com/topicmodeling.html) \n6. [Topic Modeling with Gensim (Python)](https://www.machinelearningplus.com/nlp/topic-modeling-gensim-python/) \n7. [Topic Modeling and Latent Dirichlet Allocation (LDA) in Python](https://towardsdatascience.com/topic-modeling-and-latent-dirichlet-allocation-in-python-9bf156893c24) \n8. [LDA in Python – How to grid search best topic models?](https://www.machinelearningplus.com/nlp/topic-modeling-python-sklearn-examples/) \n9. [Named Entity Recognition with NLTK and SpaCy](https://towardsdatascience.com/named-entity-recognition-with-nltk-and-spacy-8c4a7d88e7da) \n10. [I did a Kaggle competition as a semester project at uni. Here’s what I learned.](https://www.freecodecamp.org/news/i-did-a-kaggle-competition-as-a-semester-project-at-uni-heres-what-i-learned-afe36a99d309/) \n11. [Text Similarities : Estimate the degree of similarity between two texts](https://medium.com/@adriensieg/text-similarities-da019229c894) \n12. [Multi-Class Text Classification Model Comparison and Selection\n](https://towardsdatascience.com/multi-class-text-classification-model-comparison-and-selection-5eb066197568) \n13. [Multi-Class Text Classification with Scikit-Learn](https://towardsdatascience.com/multi-class-text-classification-with-scikit-learn-12f1e60e0a9f) \n14. [Machine Learning, NLP: Text Classification using scikit-learn, python and NLTK.](https://towardsdatascience.com/machine-learning-nlp-text-classification-using-scikit-learn-python-and-nltk-c52b92a7c73a) \n15. [BERT Explained: State of the art language model for NLP](https://towardsdatascience.com/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270) \n", "votes": 21 }, { "id": 667203, "postDate": "2019-11-07T00:07:58.847Z", "content": "

Thank you very much for such a nice set of information. Great collection.

", "rawMarkdown": "Thank you very much for such a nice set of information. Great collection.\n\n", "votes": 3 }, { "id": 723277, "postDate": "2020-01-19T19:28:07.137Z", "content": "

Great resources @gpreda ... Thanks for sharing

", "rawMarkdown": "Great resources @gpreda ... Thanks for sharing", "votes": 1 }, { "id": 723075, "postDate": "2020-01-19T13:23:17.167Z", "content": "

great sources thanx for share

", "rawMarkdown": "great sources thanx for share", "votes": 1 }, { "id": 691074, "postDate": "2019-12-09T15:02:42.650Z", "content": "

Wow, great sources👍

", "rawMarkdown": "Wow, great sources👍 ", "votes": 1 }, { "id": 670246, "postDate": "2019-11-11T08:01:57.177Z", "content": "

Amazing list. I also found this paper helpful.

", "rawMarkdown": "Amazing list. I also found [this paper](https://arxiv.org/abs/1705.02364v5) helpful.", "votes": 1 }, { "id": 666323, "postDate": "2019-11-06T01:55:05.733Z", "content": "

Very Informative & Helpful \nThanks @gpreda

", "rawMarkdown": "Very Informative & Helpful \nThanks @gpreda ", "votes": 1 }, { "id": 667929, "postDate": "2019-11-07T20:28:32.023Z", "content": "

The collection is gold. I'll try to find some time to look at it!\nThanks a lot for the effort!

", "rawMarkdown": "The collection is gold. I'll try to find some time to look at it!\nThanks a lot for the effort!\n", "votes": 2 }, { "id": 667898, "postDate": "2019-11-07T20:01:22.110Z", "content": "

Thanks for the articles and the nice Bot.

", "rawMarkdown": "Thanks for the articles and the nice Bot.", "votes": 2 }, { "id": 683853, "postDate": "2019-11-28T22:40:41.930Z", "rawMarkdown": "", "votes": 1, "isDeleted": true }, { "id": 680325, "postDate": "2019-11-24T13:18:50.800Z", "content": "

Thanks, that's going to help me a lot.

", "rawMarkdown": "Thanks, that's going to help me a lot.", "votes": 1 }, { "id": 668359, "postDate": "2019-11-08T10:16:42.127Z", "content": "

Thank you, great list! Diving into it

", "rawMarkdown": "Thank you, great list! Diving into it", "votes": 1 } ], "comments": [ { "id": 667203, "author_name": "Mukharbek Organokov", "author_url": "", "post_date": "2019-11-07T00:07:58.847000", "content": "

Thank you very much for such a nice set of information. Great collection.

", "votes": 3, "replies": [] }, { "id": 723277, "author_name": "Saurav Anand", "author_url": "", "post_date": "2020-01-19T19:28:07.137000", "content": "

Great resources @gpreda ... Thanks for sharing

", "votes": 1, "replies": [] }, { "id": 723075, "author_name": "mrbaysal", "author_url": "", "post_date": "2020-01-19T13:23:17.167000", "content": "

great sources thanx for share

", "votes": 1, "replies": [] }, { "id": 691074, "author_name": "DiegoJohnson", "author_url": "", "post_date": "2019-12-09T15:02:42.650000", "content": "

Wow, great sources👍

", "votes": 1, "replies": [] }, { "id": 670246, "author_name": "Shervin", "author_url": "", "post_date": "2019-11-11T08:01:57.177000", "content": "

Amazing list. I also found this paper helpful.

", "votes": 1, "replies": [] }, { "id": 666323, "author_name": "Ailurophile", "author_url": "", "post_date": "2019-11-06T01:55:05.733000", "content": "

Very Informative & Helpful \nThanks @gpreda

", "votes": 1, "replies": [] }, { "id": 667929, "author_name": "Adrian Zinovei", "author_url": "", "post_date": "2019-11-07T20:28:32.023000", "content": "

The collection is gold. I'll try to find some time to look at it!\nThanks a lot for the effort!

", "votes": 2, "replies": [] }, { "id": 667898, "author_name": "Marília Prata", "author_url": "", "post_date": "2019-11-07T20:01:22.110000", "content": "

Thanks for the articles and the nice Bot.

", "votes": 2, "replies": [] }, { "id": 683853, "author_name": "", "author_url": "", "post_date": "2019-11-28T22:40:41.930000", "content": "", "votes": 1, "replies": [] }, { "id": 680325, "author_name": "Michał Razny", "author_url": "", "post_date": "2019-11-24T13:18:50.800000", "content": "

Thanks, that's going to help me a lot.

", "votes": 1, "replies": [] }, { "id": 668359, "author_name": "Sergi Lehkyi", "author_url": "", "post_date": "2019-11-08T10:16:42.127000", "content": "

Thank you, great list! Diving into it

", "votes": 1, "replies": [] } ], "raw_markdown_by_id": { "666150": "I include here few more articles about NLP I found useful: \n![](https://algorithmia.com/blog/wp-content/uploads/2016/08/natural-language-processing-introduction.jpg) \n1. [K-Means Clustering with scikit-learn](http://jonathansoma.com/lede/algorithms-2017/classes/clustering/k-means-clustering-with-scikit-learn/) \n2. [Applied Text Analysis with Python, Chapter 4. Text Vectorization and Transformation Pipelines](https://www.oreilly.com/library/view/applied-text-analysis/9781491963036/ch04.html) \n3. [ML Data Pipelines with Custom Transformers in Python](https://towardsdatascience.com/custom-transformers-and-ml-data-pipelines-with-python-20ea2a7adb65) \n4. [Multi-Class Text Classification with Doc2Vec & Logistic Regression](https://towardsdatascience.com/multi-class-text-classification-with-doc2vec-logistic-regression-9da9947b43f4) \n5. [Topic Modeling](https://www.tidytextmining.com/topicmodeling.html) \n6. [Topic Modeling with Gensim (Python)](https://www.machinelearningplus.com/nlp/topic-modeling-gensim-python/) \n7. [Topic Modeling and Latent Dirichlet Allocation (LDA) in Python](https://towardsdatascience.com/topic-modeling-and-latent-dirichlet-allocation-in-python-9bf156893c24) \n8. [LDA in Python – How to grid search best topic models?](https://www.machinelearningplus.com/nlp/topic-modeling-python-sklearn-examples/) \n9. [Named Entity Recognition with NLTK and SpaCy](https://towardsdatascience.com/named-entity-recognition-with-nltk-and-spacy-8c4a7d88e7da) \n10. [I did a Kaggle competition as a semester project at uni. Here’s what I learned.](https://www.freecodecamp.org/news/i-did-a-kaggle-competition-as-a-semester-project-at-uni-heres-what-i-learned-afe36a99d309/) \n11. [Text Similarities : Estimate the degree of similarity between two texts](https://medium.com/@adriensieg/text-similarities-da019229c894) \n12. [Multi-Class Text Classification Model Comparison and Selection\n](https://towardsdatascience.com/multi-class-text-classification-model-comparison-and-selection-5eb066197568) \n13. [Multi-Class Text Classification with Scikit-Learn](https://towardsdatascience.com/multi-class-text-classification-with-scikit-learn-12f1e60e0a9f) \n14. [Machine Learning, NLP: Text Classification using scikit-learn, python and NLTK.](https://towardsdatascience.com/machine-learning-nlp-text-classification-using-scikit-learn-python-and-nltk-c52b92a7c73a) \n15. [BERT Explained: State of the art language model for NLP](https://towardsdatascience.com/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270) \n", "667203": "Thank you very much for such a nice set of information. Great collection.\n\n", "723277": "Great resources @gpreda ... Thanks for sharing", "723075": "great sources thanx for share", "691074": "Wow, great sources👍 ", "670246": "Amazing list. I also found [this paper](https://arxiv.org/abs/1705.02364v5) helpful.", "666323": "Very Informative & Helpful \nThanks @gpreda ", "667929": "The collection is gold. I'll try to find some time to look at it!\nThanks a lot for the effort!\n", "667898": "Thanks for the articles and the nice Bot.", "683853": "", "680325": "Thanks, that's going to help me a lot.", "668359": "Thank you, great list! Diving into it" } }