Natural Language Processing (NLP) is one of the most famous domain in the field of Machine Learning and Deep Learning. It helps the computer to understand and interpret the human’s natural language like text and speech.
Using Deep Learning model, the machine allows to process and analyze the huge amount of the natural language data. NLP learns to fill the gap between human communication and machine understanding.
Developing the application using Natural Language Processing is quite challenging as the language of the human can be English, German, Spanish, Chinese, etc. but a computer can understand only numbers.
Human language is complex and diverse. Each language has a unique set of grammar and syntax rules. An NLP is used to understand the human language, which may contain the ambiguity in the language.
A Deep Learning model is used for modelling human languages and text analysis.
Application of NLP
There are various applications of the Natural language Processing.
- Sentiment Analysis
- Language Detection
- Speech Recognition
- Automatic Text Summarization
- Machine Text Translation
- Named Entity Recognition
- Text Generation
- Part-of-speech (POS) Tagging
- Question-answering
- ChatBot
Libraries for NLP
1. Natural Language Toolkit (NLTK) – Open source Python library for Natural Language Processing.
2. spaCy – Open source software library for advanced natural language processing. The library is published under the MIT license. With spaCy, you can easily construct linguistically sophisticated statistical models for a variety of NLP problems.
3. gensim – Gensim is an open-source Python library for unsupervised topic modeling and natural language processing, using modern statistical machine learning
4. stanford CoreNLP – Stanford CoreNLP provides a set of natural language analysis tools written in Java and licensed under the GNU General Public License. It provides compatible to use it in Python.
5. Apache OpenNLP – Apache OpenNLP is an open-source Java library which is used process Natural Language text. OpenNLP supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, language detection and coreference resolution.
6. TextBlob – TextBlob is a Python (2 and 3) library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more.
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