NLP - runtimerevolution/labs GitHub Wiki

NLP is an AI methodology that combines techniques from machine learning, data science, and linguistics to process human language. This technology plays a crucial role in various applications, including speech recognition, text classification, sentiment analysis, and machine translation, among others.

Key steps involved in NLP

  1. Tokenization
  2. Text cleaning and preprocessing
  3. Part-of-Speech (PoS tagging) - in order to understand the syntactic structure of the text
  4. Text parsing
  5. Text classification

Tools and Libraries

Several tools and libraries support NLP tasks, including:

  • Natural Language Toolkit (NLTK): A leading platform for building Python programs to work with human language data.

  • spaCy: A library for advanced NLP in Python, designed specifically for production use.

  • Gensim: A library for topic modeling and document similarity analysis.

  • PyTorch-NLP: A library built on top of the PyTorch which provides tools for a range of NLP tasks (sequence tagging, language modeling, and machine translation)

Major Challenges

  1. Handling Multiple Languages: With thousands of languages worldwide, each with its own syntax, grammar, and cultural nuances, developing multilingual NLP systems is a daunting task.

  2. Training Data: High-quality annotated data is crucial for training effective NLP models. However, gathering and labeling such data is time-consuming and expensive.

  3. Ambiguity: Language can be vague and context-dependent, making it hard for NLP systems to discern the correct meaning of words and phrases.

  4. Misspellings and Errors: Human language is often imperfect, with frequent misspellings and grammatical errors. NLP systems need to be robust enough to handle these inaccuracies effectively​

  5. Evolving Language: Language continuously evolves, with new words and expressions emerging regularly. Keeping NLP models up-to-date with these changes is an ongoing challenge

References

1: "A marketer’s guide to natural language processing (NLP)" (https://sproutsocial.com/insights/natural-language-processing/)

2: "Top 8 Python Libraries For Natural Language Processing (NLP) in 2023" (https://medium.com/@soffosdotai/top-8-python-libraries-for-natural-language-processing-nlp-in-2023-5963bfa53296)

3: "Major Challenges of Natural Language Processing" (https://www.geeksforgeeks.org/major-challenges-of-natural-language-processing/)