Named entity recognition (NER) is the task of identifying and categorizing key information (entities) in text (from Wikipedia).
\nKeyphrase extraction is a textual information processing task concerned with the automatic extraction of representative and characteristic phrases from a document that express all the key aspects of its content (from ArXiv - A Review of Keyphrase Extraction, see below list of papers).
\n![\"\"](\"https://cdn-images-1.medium.com/max/960/1*bsR0yynF5DvUogiBEq1iwg.jpeg\")
\nHere are some materials (papers, blogs, articles, projects) about key phrase extraction and NER. I also added links to few past Kaggle competition for NLP from which you can extract ideas and insights.
Named entity recognition (NER) is the task of identifying and categorizing key information (entities) in text (from Wikipedia).
\nKeyphrase extraction is a textual information processing task concerned with the automatic extraction of representative and characteristic phrases from a document that express all the key aspects of its content (from ArXiv - A Review of Keyphrase Extraction, see below list of papers).
\n
\nHere are some materials (papers, blogs, articles, projects) about key phrase extraction and NER. I also added links to few past Kaggle competition for NLP from which you can extract ideas and insights.
very useful notebook thanks for sharing .
", "rawMarkdown": "very useful notebook thanks for sharing .", "votes": 1 }, { "id": 1254160, "postDate": "2021-03-27T11:43:25.063Z", "content": "Thanks for sharing the resources, appreciate it.
", "rawMarkdown": "Thanks for sharing the resources, appreciate it.", "votes": 1 }, { "id": 1253286, "postDate": "2021-03-26T15:04:11.447Z", "content": "thanks, I was searching for NER to solve this problem. you made it easy for me
", "rawMarkdown": "thanks, I was searching for NER to solve this problem. you made it easy for me", "votes": 1, "replies": [ { "id": 1337192, "postDate": "2021-06-05T13:20:32.457Z", "content": "Cool. So did you implement NER. What was the score??
", "rawMarkdown": "Cool. So did you implement NER. What was the score??" } ] }, { "id": 1252930, "postDate": "2021-03-26T07:45:59.010Z", "content": "Thanks for sharing all this helpful stuff.
", "rawMarkdown": "Thanks for sharing all this helpful stuff.", "votes": 1, "replies": [ { "id": 1252932, "postDate": "2021-03-26T07:48:49.587Z", "content": "Stay tuned, I will keep update this topic with new articles, projects and research papers. If you have any suggestions about content to add, please do not hesitate to comment here.
", "rawMarkdown": "Stay tuned, I will keep update this topic with new articles, projects and research papers. If you have any suggestions about content to add, please do not hesitate to comment here. ", "votes": 1 } ] }, { "id": 1252668, "postDate": "2021-03-25T22:59:44.797Z", "content": "Thanks a lot for sharing this.
", "rawMarkdown": "Thanks a lot for sharing this.", "votes": 1, "replies": [ { "id": 1252675, "postDate": "2021-03-25T23:15:46.357Z", "content": "Thank you for your appreciation. I am still updating this topic with additional information so, stay tuned!
", "rawMarkdown": "Thank you for your appreciation. I am still updating this topic with additional information so, stay tuned!", "votes": 2 } ] }, { "id": 1251662, "postDate": "2021-03-25T02:19:55.893Z", "content": "Coooooool！
\nbtw,I never fear :)
Thank you for your feedback. I will update the list, so stay tuned :-).
", "rawMarkdown": "Thank you for your feedback. I will update the list, so stay tuned :-)." } ] }, { "id": 1321689, "postDate": "2021-05-24T21:30:04.190Z", "content": "nice recap, much appreciated
", "rawMarkdown": "nice recap, much appreciated" }, { "id": 1310529, "postDate": "2021-05-16T17:59:26.797Z", "content": "Hi, I had two questions,
\nI'm a beginner and I don't understand how one could use a BERT model, does it not require the internet to say download the bert-cased model or to install simpletransformer, etc. and also the time bert takes is extremely long so say if someone opts for an upgrade to google cloud AI notebooks and does the work there, would that be of no use since the notebook will be run privately by Kaggle again and that should be under 9 hours or do u submit the notebook directly from cloud AI.
\nThank You.
\n(Also yes still trying to work around kaggle, relatively new to the platform)
You can load BERT in a notebook using datasets on Kaggle. Look in the top right corner of your notebook and click the add data link and search for BERT uncased etc…
", "rawMarkdown": "You can load BERT in a notebook using datasets on Kaggle. Look in the top right corner of your notebook and click the add data link and search for BERT uncased etc...", "votes": 1 }, { "id": 1310701, "postDate": "2021-05-16T21:17:21.093Z", "content": "thank you so much :)
", "rawMarkdown": "thank you so much :)" } ] }, { "id": 1283075, "postDate": "2021-04-24T14:56:39.013Z", "content": "This is super helpful. Thanks for sharing these resources.
", "rawMarkdown": "This is super helpful. Thanks for sharing these resources. " }, { "id": 1267765, "postDate": "2021-04-08T18:57:21.663Z", "content": "Tks for sharing!
", "rawMarkdown": "Tks for sharing!" }, { "id": 1259626, "postDate": "2021-04-01T14:53:21.897Z", "content": "A good collection for beginner like me, thanks a lot.
", "rawMarkdown": "A good collection for beginner like me, thanks a lot." }, { "id": 1255587, "postDate": "2021-03-28T23:53:34.217Z", "content": "Thanks, Gabriel that's so helpful for me
", "rawMarkdown": "Thanks, Gabriel that's so helpful for me" }, { "id": 1252632, "postDate": "2021-03-25T21:28:26.880Z", "content": "Note: updated list of blogs, projects, papers.
", "rawMarkdown": "Note: updated list of blogs, projects, papers." }, { "id": 1310477, "postDate": "2021-05-16T17:25:56.960Z", "rawMarkdown": "", "isDeleted": true }, { "id": 1253007, "postDate": "2021-03-26T09:33:39.980Z", "content": "thanks for sharing,very userful 👍
", "rawMarkdown": "thanks for sharing,very userful 👍", "votes": 2 }, { "id": 1761232, "postDate": "2022-04-19T18:19:41.820Z", "content": "Thank you 👍 Iqbal….
", "rawMarkdown": "Thank you 👍 Iqbal...." }, { "id": 1328781, "postDate": "2021-05-30T14:28:44.117Z", "content": "This is helpful. Thanks for sharing.
", "rawMarkdown": "This is helpful. Thanks for sharing. " }, { "id": 1312727, "postDate": "2021-05-18T08:03:24.850Z", "content": "Thanks for sharing
", "rawMarkdown": "Thanks for sharing" }, { "id": 1274389, "postDate": "2021-04-15T09:02:42.280Z", "content": "Thanks for sharing!
", "rawMarkdown": "Thanks for sharing!" }, { "id": 1268576, "postDate": "2021-04-09T14:50:13.170Z", "content": "Thanks for sharing
", "rawMarkdown": "Thanks for sharing" }, { "id": 1283237, "postDate": "2021-04-24T18:03:14.840Z", "content": "Thanks for sharing.
", "rawMarkdown": "Thanks for sharing.", "isDeleted": true } ], "comments": [ { "id": 1761083, "author_name": "Iqbal Ali", "author_url": "", "post_date": "2022-04-19T17:03:26.290000", "content": "very useful notebook thanks for sharing .
", "votes": 1, "replies": [] }, { "id": 1254160, "author_name": "Sourav Banerjee", "author_url": "", "post_date": "2021-03-27T11:43:25.063000", "content": "Thanks for sharing the resources, appreciate it.
", "votes": 1, "replies": [] }, { "id": 1253286, "author_name": "masoud parpanchi", "author_url": "", "post_date": "2021-03-26T15:04:11.447000", "content": "thanks, I was searching for NER to solve this problem. you made it easy for me
", "votes": 1, "replies": [ { "id": 1337192, "author_name": "Vikrant", "author_url": "", "post_date": "2021-06-05T13:20:32.457000", "content": "Cool. So did you implement NER. What was the score??
", "votes": 0, "replies": [] } ] }, { "id": 1252930, "author_name": "HammadAsif", "author_url": "", "post_date": "2021-03-26T07:45:59.010000", "content": "Thanks for sharing all this helpful stuff.
", "votes": 1, "replies": [ { "id": 1252932, "author_name": "Gabriel Preda", "author_url": "", "post_date": "2021-03-26T07:48:49.587000", "content": "Stay tuned, I will keep update this topic with new articles, projects and research papers. If you have any suggestions about content to add, please do not hesitate to comment here.
", "votes": 1, "replies": [] } ] }, { "id": 1252668, "author_name": "Harshit Gupta", "author_url": "", "post_date": "2021-03-25T22:59:44.797000", "content": "Thanks a lot for sharing this.
", "votes": 1, "replies": [ { "id": 1252675, "author_name": "Gabriel Preda", "author_url": "", "post_date": "2021-03-25T23:15:46.357000", "content": "Thank you for your appreciation. I am still updating this topic with additional information so, stay tuned!
", "votes": 2, "replies": [] } ] }, { "id": 1251662, "author_name": "Mingjie Wang", "author_url": "", "post_date": "2021-03-25T02:19:55.893000", "content": "Coooooool！
\nbtw,I never fear :)
Thank you for your feedback. I will update the list, so stay tuned :-).
", "votes": 0, "replies": [] } ] }, { "id": 1321689, "author_name": "", "author_url": "", "post_date": "2021-05-24T21:30:04.190000", "content": "nice recap, much appreciated
", "votes": 0, "replies": [] }, { "id": 1310529, "author_name": "BLING_LEGEND", "author_url": "", "post_date": "2021-05-16T17:59:26.797000", "content": "Hi, I had two questions,
\nI'm a beginner and I don't understand how one could use a BERT model, does it not require the internet to say download the bert-cased model or to install simpletransformer, etc. and also the time bert takes is extremely long so say if someone opts for an upgrade to google cloud AI notebooks and does the work there, would that be of no use since the notebook will be run privately by Kaggle again and that should be under 9 hours or do u submit the notebook directly from cloud AI.
\nThank You.
\n(Also yes still trying to work around kaggle, relatively new to the platform)
You can load BERT in a notebook using datasets on Kaggle. Look in the top right corner of your notebook and click the add data link and search for BERT uncased etc…
", "votes": 1, "replies": [] }, { "id": 1310701, "author_name": "BLING_LEGEND", "author_url": "", "post_date": "2021-05-16T21:17:21.093000", "content": "thank you so much :)
", "votes": 0, "replies": [] } ] }, { "id": 1283075, "author_name": "Kartik B Bhargav", "author_url": "", "post_date": "2021-04-24T14:56:39.013000", "content": "This is super helpful. Thanks for sharing these resources.
", "votes": 0, "replies": [] }, { "id": 1267765, "author_name": "Gabriele Bellini", "author_url": "", "post_date": "2021-04-08T18:57:21.663000", "content": "Tks for sharing!
", "votes": 0, "replies": [] }, { "id": 1259626, "author_name": "Ye Ning", "author_url": "", "post_date": "2021-04-01T14:53:21.897000", "content": "A good collection for beginner like me, thanks a lot.
", "votes": 0, "replies": [] }, { "id": 1255587, "author_name": "ِِAhmed El Adly", "author_url": "", "post_date": "2021-03-28T23:53:34.217000", "content": "Thanks, Gabriel that's so helpful for me
", "votes": 0, "replies": [] }, { "id": 1252632, "author_name": "Gabriel Preda", "author_url": "", "post_date": "2021-03-25T21:28:26.880000", "content": "Note: updated list of blogs, projects, papers.
", "votes": 0, "replies": [] }, { "id": 1310477, "author_name": "", "author_url": "", "post_date": "2021-05-16T17:25:56.960000", "content": "", "votes": 0, "replies": [] }, { "id": 1253007, "author_name": "qiaqia", "author_url": "", "post_date": "2021-03-26T09:33:39.980000", "content": "thanks for sharing,very userful 👍
", "votes": 2, "replies": [] }, { "id": 1761232, "author_name": "Henry Javier", "author_url": "", "post_date": "2022-04-19T18:19:41.820000", "content": "Thank you 👍 Iqbal….
", "votes": 0, "replies": [] }, { "id": 1328781, "author_name": "Kartik B Bhargav", "author_url": "", "post_date": "2021-05-30T14:28:44.117000", "content": "This is helpful. Thanks for sharing.
", "votes": 0, "replies": [] }, { "id": 1312727, "author_name": "Ulrica", "author_url": "", "post_date": "2021-05-18T08:03:24.850000", "content": "Thanks for sharing
", "votes": 0, "replies": [] }, { "id": 1274389, "author_name": "YANG Zhanye", "author_url": "", "post_date": "2021-04-15T09:02:42.280000", "content": "Thanks for sharing!
", "votes": 0, "replies": [] }, { "id": 1268576, "author_name": "Alex", "author_url": "", "post_date": "2021-04-09T14:50:13.170000", "content": "Thanks for sharing
", "votes": 0, "replies": [] }, { "id": 1283237, "author_name": "", "author_url": "", "post_date": "2021-04-24T18:03:14.840000", "content": "Thanks for sharing.
", "votes": 0, "replies": [] } ], "raw_markdown_by_id": { "1251009": "\n#### 💥Introduction\n\nNamed entity recognition (NER) is the task of identifying and categorizing key information (entities) in text (from [Wikipedia](https://en.wikipedia.org/wiki/Named-entity_recognition)).\nKeyphrase extraction is a textual information processing task concerned with the automatic extraction of representative and characteristic phrases from a document that express all the key aspects of its content (from [ArXiv - A Review of Keyphrase Extraction](https://arxiv.org/pdf/1905.05044.pdf), see below list of papers). \n\n\n\nHere are some materials (papers, blogs, articles, projects) about key phrase extraction and NER. I also added links to few past Kaggle competition for NLP from which you can extract ideas and insights.\n\n\n#### 💥Blogs, articles\n\n* [Towards Data Science/Medium - Keyword Extraction with BERT](https://towardsdatascience.com/keyword-extraction-with-bert-724efca412ea) \nA minimal method for extracting keywords and keyphrases\n* [Towards Data Science/Medium - Keyword Extraction: from TF-IDF to BERT](https://towardsdatascience.com/keyword-extraction-python-tf-idf-textrank-topicrank-yake-bert-7405d51cd839)\nHow to perform keyword extraction in Python with TF-IDF, TextRank, TopicRank, YAKE!, and KeyBERT\n* [Towards Data Science/Medium - Named Entity Recognition in NLP](https://towardsdatascience.com/named-entity-recognition-in-nlp-be09139fa7b8) \nReal-world use cases, models, methods: from simple to advanced\n* [Towards Data Science/Medium - Named Entity Recognition with NLTK and SpaCy](https://towardsdatascience.com/named-entity-recognition-with-nltk-and-spacy-8c4a7d88e7da)\nNER is used in many fields in Natural Language Processing (NLP)\n* [KDnuggets - Named Entity Recognition: A Practitioner’s Guide to NLP](https://www.kdnuggets.com/2018/08/named-entity-recognition-practitioners-guide-nlp-4.html) \nNamed entity recognition (NER) is used in information extraction to identify and segment the named entities and classify or categorize them under various predefined classes.\n* [Microsoft Docs - Key phrase extraction model](https://docs.microsoft.com/en-us/ai-builder/prebuilt-key-phrase)\nKey phrase extraction prebuilt model identifies the main points in a text document\n* [Medium - Insights on Key phrase Extraction from Unstructured Text](https://medium.com/@kanimozhiu/insights-on-key-phrase-extraction-from-unstructured-text-ffa62b728da)\nReview of evaluation approaches, open issues, lists supervised and unsupervised approaches, includes a compilation of open source and commercial software developed for key phrase extraction\n* [Medium - RAKE: Rapid Automatic Keyword Extraction Algorithm](https://medium.datadriveninvestor.com/rake-rapid-automatic-keyword-extraction-algorithm-f4ec17b2886c)\nRAKE uses stop words and phrase delimiters to partition the document into candidate keywords\n* [Papers with Code - Keyword Extraction](https://paperswithcode.com/task/keyword-extraction/codeless)\n\n#### 💥Projects\n\n* [GitHub - BERT for Keyphrase Extraction (Pytorch)](https://github.com/thunlp/BERT-KPE) \nThis repository provides the code of the paper Joint Keyphrase Chunking and Salience Ranking with BERT (see paper list).\n* [GitHub - KeyBERT](https://github.com/MaartenGr/KeyBERT)\nKeyBERT is a minimal and easy-to-use keyword extraction technique that leverages BERT embeddings to create keywords and keyphrases that are most similar to a document.\n* [GitHub - Keyphrase Extraction using Sentence Embeddings](https://github.com/swisscom/ai-research-keyphrase-extraction) \nThis is an implementation of the paper Simple Unsupervised Keyphrase Extraction using Sentence Embeddings (see paper list).\n* [GitHub - Key2Vec](https://github.com/MarkSecada/key2vec)\nPython implementation of Mahata, Kuriakose, et. al.'s Key2Vec - see below in the paper list\n* [GitHub - Yet Another Keyword Extractor (Yake)](https://github.com/LIAAD/yake)\n* [GitHub - Flair](https://github.com/flairNLP/flair) \n* [GitHub - HUNER implementation inside of Flair framework](https://github.com/hu-ner/huner)\n\n#### 💥Papers \n\n* [ArXiv - Joint Keyphrase Chunking and Salience Ranking with BERT](https://arxiv.org/pdf/2004.13639.pdf)\n* [ArXiv - A Review of Keyphrase Extraction](https://arxiv.org/pdf/1905.05044.pdf)\n* [ArXiv - Keyphrase Extraction from Scholarly Articles as Sequence Labeling using Contextualized Embeddings](https://arxiv.org/pdf/1910.08840.pdf) \n* [ArXiv - Joint Keyphrase Chunking and Salience Ranking with BERT](https://arxiv.org/pdf/2004.13639.pdf) \n* [ArXiv - Simple Unsupervised Keyphrase Extraction using Sentence Embeddings](https://arxiv.org/pdf/1801.04470.pdf) \n* [ACL Web - Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings](https://www.aclweb.org/anthology/N18-2100.pdf)\n* [ScienceDirect - A Graph-based Approach of Automatic Keyphrase Extraction](https://www.sciencedirect.com/science/article/pii/S1877050917303629)\n* [SpringerLink - Keyphrase Extraction as Sequence Labeling Using Contextualized Embeddings](https://link.springer.com/chapter/10.1007/978-3-030-45442-5_41)\n* [SpringerLink - Learning Algorithms for Keyphrase Extraction](https://link.springer.com/article/10.1023/A:1009976227802)\n\n#### 💥Past Kaggle NLP Competitions\n\n* [Google QUEST Q&A Labeling](https://www.kaggle.com/c/google-quest-challenge/overview)\nImproving automated understanding of complex question answer content\n\n* [TensorFlow 2.0 Question Answering](https://www.kaggle.com/c/tensorflow2-question-answering/)\nIdentify the answers to real user questions about Wikipedia page content\n\n* [Jigsaw Unintended Bias in Toxicity Classification](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification)\nDetect toxicity across a diverse range of conversations\n\n* [Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge)\nIdentify and classify toxic online comments\n", "1761083": "very useful notebook thanks for sharing .", "1254160": "Thanks for sharing the resources, appreciate it.", "1253286": "thanks, I was searching for NER to solve this problem. you made it easy for me", "1252930": "Thanks for sharing all this helpful stuff.", "1252668": "Thanks a lot for sharing this.", "1251662": "Coooooool！\nbtw,I never fear :)", "1321689": "nice recap, much appreciated", "1310529": "Hi, I had two questions, \nI'm a beginner and I don't understand how one could use a BERT model, does it not require the internet to say download the bert-cased model or to install simpletransformer, etc. and also the time bert takes is extremely long so say if someone opts for an upgrade to google cloud AI notebooks and does the work there, would that be of no use since the notebook will be run privately by Kaggle again and that should be under 9 hours or do u submit the notebook directly from cloud AI.\nThank You.\n(Also yes still trying to work around kaggle, relatively new to the platform)", "1283075": "This is super helpful. Thanks for sharing these resources. ", "1267765": "Tks for sharing!", "1259626": "A good collection for beginner like me, thanks a lot.", "1255587": "Thanks, Gabriel that's so helpful for me", "1252632": "Note: updated list of blogs, projects, papers.", "1310477": "", "1253007": "thanks for sharing,very userful 👍", "1761232": "Thank you 👍 Iqbal....", "1328781": "This is helpful. Thanks for sharing. ", "1312727": "Thanks for sharing", "1274389": "Thanks for sharing!", "1268576": "Thanks for sharing", "1283237": "Thanks for sharing." } }