This repository serves as a comprehensive tutorial and personal journey log for Natural Language Processing (NLP), covering a vast array of techniques from fundamental text preprocessing to advanced transformer models and graph embeddings. It is targeted at engineers and researchers looking to understand and implement various NLP concepts, offering a structured overview of state-of-the-art methods and their associated research.

How It Works

The repository is organized thematically, detailing core NLP tasks such as tokenization, stemming, lemmatization, and spell checking. It then delves into text representation, covering traditional methods like Bag-of-Words and modern approaches including Word2Vec, GloVe, fastText, and various contextualized embeddings like ELMo and BERT. The structure also includes sections on sentence-level embeddings, document-level analysis, and specific NLP problems like Named Entity Recognition (NER) and Text Summarization.

Quick Start & Requirements

• Install: No explicit installation instructions or commands are provided. The repository appears to be a collection of notes, code snippets, and references rather than a runnable library.
• Prerequisites: Likely requires Python and common NLP libraries (e.g., NLTK, spaCy, Hugging Face Transformers) for executing any provided code. Specific model implementations may require GPU acceleration and corresponding CUDA versions.
• Resources: Setup time and resource footprint are not specified, as it's primarily a reference repository.

Highlighted Details

• Extensive coverage of text representation methods, from traditional techniques to advanced contextual embeddings like BERT, GPT, and XLNet.
• Detailed sections on specific NLP problems including NER, OCR, Text Summarization, and Emotion Recognition.
• Includes overviews of graph embeddings (e.g., DeepWalk, node2vec, GCN) and meta-learning concepts relevant to NLP.
• Provides links to research papers and source code for many of the discussed techniques.

Maintenance & Community

• The repository is a personal log, with no explicit mention of active maintenance, community channels, or contributor information beyond the owner.

Licensing & Compatibility

• The repository itself does not specify a license. The included code snippets and references to external libraries would be subject to their respective licenses.

Limitations & Caveats

• This repository is presented as a personal learning log and tutorial collection, not a cohesive, runnable library. Users will need to extract and adapt code, and manage dependencies themselves.
• There are no explicit benchmarks or performance comparisons provided for the various methods discussed.
• The depth of explanation for each topic varies, with many entries linking to external "Medium" articles or papers.