This list is not a definitive ranking of libraries and frameworks, but rather a subjective guide that might help you design your NLP learning journey. It’s not easy to measure difficulty or compare different libraries or frameworks that serve different purposes, have different design philosophies, and are suited for different types of tasks, but following is just a rough guide:

1. TextBlob

TextBlob is often considered the easiest NLP library to use for processing and analyzing textual data. It is especially for beginners who are new to NLP. It’s easy to install (with a simple ‘pip’ command) and it comes with pre-trained models for most common NLP tasks, such as POS tagging, noun-phrase extraction, and sentiment analysis. It also includes convenient features, like tokenization, POS tagging, noun-phrase extraction, language detection, spell checking, translation and sentiment analysis.

2. SpaCy

SpaCy is also easy to use library with a simple and intuitive API, designed for production use. It also comes with pre-trained models for various languages, that can be used for tasks, such as tokenization, POS tagging, NER, etc. Besides these, spaCy provides visualization tools for linguistics features, parse trees and dependencies.

3. NLTK (Natural Language Toolkit)

NLTK is a comprehensive library that provides a wide range of NLP tools and easy-to-use interfaces to over 50 corpora and lexical resources. Some of the key features NLTK includes are tokenization, POS tagging, NER, parsing, sentiment analysis, language detection and other text analysis tools (frequency distribution analysis, concordance search, collocation identification). It also includes utilities for machine learning, which can be used to create custom NLP models for tasks like text classification and clustering. It is highly customizable, allowing users to experiment with different NLP techniques and algorithms.

4. Transformers (Hugging Face)

Transformers is a popular library by Hugging Face that provides pre-trained transformer-based models like BERT, GPT, BART, RoBERTa, etc. These models have achieved state-of-the-art results on various NLP tasks like text classification, NER, text generation, text similarity, language translation, summarization, and question answering. Transformers can also be employed to build chatbots and conversational agents. You can also fine-tune pre-trained model for custom nlp tasks.

5. Gensim

Gensim is a library for topic modeling and document similarity analysis. It’s particularly well-suited for building and training word embeddings, such as Word2Vec and FastText. While some understanding of vector spaces and word embeddings may be necessary, users can train word embeddings or use pre-trained embeddings with minimal effort. It’s also scalable and efficient, making it suitable for processing large text corpora. 

6. Scikit-learn

Scikit-learn is a machine learning library that can be used for some NLP tasks, such as text preprocessing, text classification, text clustering, sentiment analysis, and feature extraction. It provides tools for feature extraction and model training. It’s not as specialized or feature-rich as some dedicated NLP libraries (spaCy, NLTK), but it can be useful especially if you want to integrate NLP tasks into your broader machine learning workflows.

7. AllenNLP

AllenNLP is a deep learning library specifically designed for NLP research. It offers pre-built components for various NLP tasks (text classification, NER, coreference resolution, dependency parsing, and more) and allows you to build custom models and experiments. Allen NLP is particularly well-suited for tasks that benefit from neural network architectures and transfer learning with pre-trained models, as it is centered around deep learning techniques and is built on the PyTorch framework.

8. PyTorch and TensorFlow

PyTorch and TensorFlow are deep learning frameworks that offer flexible tools for building and training neural network models for NLP tasks. They are similar in their application to NLP tasks as they serve as the foundational deep learning frameworks for developing and fine-tuning models that understand and generate human language, enabling a wide range of language-related applications.

9. Stanza (formerly: StanfordNLP)

Stanza is a Python NLP library that offers an extensive set of pre-trained models and tools for diverse NLP tasks, including tokenization, POS tagging, NER, and dependency parsing. It’s renowned for its language processing capabilities across many human languages, using it effectively may require a solid understanding of NLP concepts and linguistic principles.

10. OpenNLP

OpenNLP is a Java-based library, so it might be challenging for beginners not familiar with Java programming. However, it offers a wide range of NLP features and tools, including tokenization, POS tagging, NER, chunking, and parsing. OpenNLP may have fewer pre-trained models compared to other libraries like spaCy or Hugging Face’s Transformers, which can make it more challenging to work on certain NLP tasks out-of-the-box.

11. Flair

Flair is an NLP library built on PyTorch, focusing on contextual word embeddings and providing tools for standard NLP tasks. It is known for its state-of-the-art results and is particularly suited for applications requiring context-aware language understanding. It requires a strong understanding of deep learning concepts and the ability to fine-tune models.