kavehkarimadini/arc_101_getting_started_with_nlp

ARC_101_Getting_Started_With_NLP

---

Abstract:

Natural Language Processing (NLP) in Python involves the use of libraries like NLTK, SpaCy, and TextBlob for tasks such as text processing, classification, tagging, parsing, semantic reasoning, and wrappers over industrial-strength NLP libraries. Python's simplicity and wide range of NLP libraries make it an ideal choice for NLP tasks.

---

Introduction

Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and humans through natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of human language in a valuable way.

Python has been widely used for NLP tasks due to its simplicity and the variety of libraries it offers for NLP. These libraries include:

1. NLTK (Natural Language Toolkit): NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning.

2. SpaCy: SpaCy is an open-source software library for advanced NLP. It is designed specifically for production use and helps build applications that process and understand large volumes of text. It can be used to build information extraction or natural language understanding systems, or to pre-process text for deep learning.

3. TextBlob: TextBlob is a Python library for processing textual data. It provides a simple API for diving into common NLP tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more.

4. Gensim: Gensim is a robust open-source vector space modeling and topic modeling toolkit implemented in Python. It uses NumPy, SciPy and optionally Cython for performance. It is specifically designed to handle large text collections, using data streaming and efficient incremental algorithms.

5. Transformers: Transformers, by Hugging Face, provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation, and more in 100+ languages. Its aim is to make cutting-edge NLP easier to use for everyone.

These libraries make Python an excellent choice for NLP tasks, providing powerful tools for tasks ranging from simple text processing to complex machine translation.

---

Order of Notebooks to work with:

1. GS_NLP_CH_01_Intro.ipynb
2. GS_NLP_CH_02_Spam_text_classification