GitHub - DragonEmperorMk/Natural-Language-Processing-NLP-Experiments: Natural language processing helps computers communicate with humans in their own language and scales other language-related tasks. For example, NLP makes it possible for computers to read text, hear speech, interpret it, measure sentiment and determine which parts are important

Natural Language Processing (NLP) is a field of computer science, artificial intelligence, and linguistics concerned with giving computers the ability to understand, interpret, and manipulate human language [1: https://aws.amazon.com/what-is/nlp/, 4: https://www.simplilearn.com/tutorials/artificial-intelligence-tutorial/what-is-natural-language-processing-nlp, 6: https://www.geeksforgeeks.org/natural-language-processing-overview/].

Here are some of the core functionalities of NLP:

Understanding the meaning of text: This involves tasks like sentiment analysis, which determines the emotional tone of a piece of writing, and recognizing the intent behind a question or statement [2: https://towardsdatascience.com/basic-concepts-of-natural-language-processing-nlp-models-and-python-implementation-88a589ce1fc0, 3: https://www.techtarget.com/searchenterpriseai/definition/natural-language-processing-NLP]. Generating human-like text: This can be used for tasks like machine translation, which allows computers to translate text from one language to another, and chatbots, which can hold conversations with humans [5: https://www.ibm.com/topics/natural-language-processing]. NLP is a rapidly evolving field with a wide range of applications, from spam detection and social media analysis to machine translation and virtual assistants.

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
01_Tokenization 2.ipynb		01_Tokenization 2.ipynb
02_Tokenization_Techniques 2.ipynb		02_Tokenization_Techniques 2.ipynb
03_Tokenisation_Stopwords_Delimiters 1.ipynb		03_Tokenisation_Stopwords_Delimiters 1.ipynb
04_Stopwords_Removal.ipynb		04_Stopwords_Removal.ipynb
05_Stopwords_NIL_JUNK 2.ipynb		05_Stopwords_NIL_JUNK 2.ipynb
06_Lemmatization.ipynb		06_Lemmatization.ipynb
06_Stemming 1.ipynb		06_Stemming 1.ipynb
07A_CleanTokenize_Tweets 1.ipynb		07A_CleanTokenize_Tweets 1.ipynb
07B_CleanTokenize_Tweets 1.ipynb		07B_CleanTokenize_Tweets 1.ipynb
08_Extracting_Usernames 1.ipynb		08_Extracting_Usernames 1.ipynb
09_Extracting_Top_Common_Names 1.ipynb		09_Extracting_Top_Common_Names 1.ipynb
10_Extracting_Nouns 1.ipynb		10_Extracting_Nouns 1.ipynb
11_Cost_Function_Similarity _NLTK.ipynb		11_Cost_Function_Similarity _NLTK.ipynb
11_Similarity_words_spaCy.ipynb		11_Similarity_words_spaCy.ipynb
12_Word_Mover_Distance 1.ipynb		12_Word_Mover_Distance 1.ipynb
13_Replcaing_Pronouns_Objects 1.ipynb		13_Replcaing_Pronouns_Objects 1.ipynb
14_Topic_Exctaction_NMF 1.ipynb		14_Topic_Exctaction_NMF 1.ipynb
15_Word_Vector_Extraction 1 (1).ipynb		15_Word_Vector_Extraction 1 (1).ipynb
16_Implementing_Word_Embedding.ipynb		16_Implementing_Word_Embedding.ipynb
README.md		README.md
demo.txt		demo.txt
sample_text.txt		sample_text.txt

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