Table of contents
1. Natural language processing NLP
1.1 NLP Chinese and English comparison (double)
3.1 List of NLP open source tools
4. Natural language text classification model code
1. Natural language processing NLP
1.1 NLP Chinese and English comparison (double)
| Chinese(1) | Natural Language Processing (NLP) is a branch of computer science and artificial intelligence that aims to enable computers to understand, parse, generate and manipulate human language. It combines knowledge from multiple disciplines such as computer science, linguistics, and mathematical statistics to provide people with powerful text analysis and language processing tools. NLP is widely used, such as machine translation, sentiment analysis, automatic question answering, speech recognition, text classification and information extraction, etc. |
| English (1) | Natural Language Processing (NLP) is a branch of computer science and artificial intelligence aimed at enabling computers to understand, parse, generate, and manipulate human language. It combines knowledge from multiple disciplines such as computer science, linguistics, and mathematical statistics, providing powerful text analysis and language processing tools for people. NLP is widely used, such as machine translation, sentiment analysis, automatic question answering, speech recognition, text classification, and information extraction. |
| Chinese(2) | NLP stands for Natural Language Processing. It is a branch of artificial intelligence that focuses on enabling computers to understand, interpret and generate human language. This includes teaching computers to recognize speech patterns, syntax, grammar and context, and to respond appropriately to human language input. NLP has various applications such as chatbots, speech recognition, language translation, sentiment analysis, etc. NLP is becoming increasingly important in fields such as customer service, healthcare, education, and marketing. |
| English (2) | NLP stands for Natural Language Processing. It is a branch of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. This involves teaching computers to recognize speech patterns, syntax, grammar, and context, as well as to respond appropriately to human language input. NLP has various applications such as chatbots, speech recognition, language translation, sentiment analysis, and more. NLP is becoming increasingly important in fields such as customer service, healthcare, education, and marketing, among others. |
1.2 Related articles
2.NLP corpus
2.1 NLP corpus enumeration
| 1. | Commonly used English corpora: Penn Treebank, GigaWord, Brown Corpus, Reuters Corpus, Web Text, etc. |
| 2. | Chinese corpus: People's Daily corpus, Xinhua News Agency corpus, Commonly Used Chinese Words (CCG) corpus, Fudan University Chinese text classification corpus, etc. |
| 3. | Social media corpus: Twitter corpus, Facebook corpus, Reddit corpus, etc. |
| 4. | Multilingual corpora: Europarl, Tatoeba, Wikipedia, OPUS, etc. |
| 5. | Medical corpus: MEDLINE, PubMed, etc. |
| 6. | Legal corpus: American legal document corpus, British legal document corpus, etc. |
| 7. | Science and technology corpus: Google Books Ngrams, Google News Dataset, etc. |
| 8. | Speech recognition corpus: TIMIT, CSJ, etc. |
2.2 Related articles
Chinese Academy of Sciences Corpus 
https://blog.csdn.net/SYC20110120/article/details/132722058
3.NLP open source tools
3.1 List of NLP open source tools
| 1. | NLTK (Natural Language Toolkit): A commonly used NLP tool library in Python, including multiple functions such as word segmentation, part-of-speech tagging, named entity recognition, text classification, and syntax analysis. |
| 2. | spaCy: Another NLP tool library in Python, fast, feature-rich, and supports multi-language processing |
| 3. | Stanford NLP: An NLP tool set developed by Stanford University, including multiple modules such as word segmentation, part-of-speech tagging, syntactic analysis, and named entity recognition. |
| 4. | OpenNLP: A Java NLP tool set developed by Apache, including multiple modules such as word segmentation, named entity recognition, and syntax analysis. |
| 5. | Gensim: a natural language processing library in Python, used for text similarity calculation, document topic modeling and other applications |
| 6. | CoreNLP: part of the NLP tool set developed by Stanford University, supports multi-language processing, including multiple modules such as word segmentation, part-of-speech tagging, syntactic analysis, sentiment analysis, etc. |
| 7. | FastText: Facebook’s open source NLP tool, which can perform tasks such as word vector training, text classification, and text clustering. |
| 8. | AllenNLP: An NLP tool library based on PyTorch, which provides multiple pre-trained models, including text classification, reading comprehension and other tasks. |
3.2 Related articles
Introduction to NLP open source tools https://blog.csdn.net/SYC20110120/article/details/132646555
4. Natural language text classification model code
5.Graphic NLP
6. Application of NLP in life
| 1. | Smart Assistants: Smart assistants like Siri, Alexa, and Google Assistant are built on NLP technology. They can understand human language and answer questions or perform tasks |
| 2. | Speech recognition: NLP technology is used to recognize the words we speak and convert them into editable text. Speech recognition can be used for voice assistants, voice search, voice control, etc. |
| 3. | Machine Translation: NLP technology can be used to translate one language into another. This technology is very important for cross-border exchanges such as trade and tourism |
| 4. | Information extraction: NLP can extract key information from large amounts of text, for example, extracting key events from news reports or extracting terms and details from business contracts |
| 5. | Sentiment analysis: NLP technology can be used to evaluate the emotional color of large amounts of data, for example, analyzing the emotional color of comments posted on social media, etc. |
| 6. | Question and answer system: NLP technology can be used to build an automatic question and answer system. For example, in the field of customer service, users can communicate with robots through natural language and get answers to solve problems. |
| 7. | Natural language generation: NLP technology can be used to automatically generate articles, summaries, text messages, etc. This technology has applications in news reporting, advertising, social media, etc. |