NLP-Interview-Notes
This resource offers carefully curated study notes and materials for natural language processing (NLP) interview preparation. It covers a broad array of interview questions across various NLP domains and provides thorough insights into algorithms such as Hidden Markov Model (HMM), Maximum Entropy Markov Model (MEMM), and Conditional Random Fields (CRF). Designed to support both novices and experienced professionals, the project addresses crucial topics like named entity recognition, relationship extraction, event extraction, and pre-training methods like TF-IDF and Word2Vec. Each section presents typical interview questions, explanations, and solutions, forming a comprehensive reference for NLP enthusiasts preparing for technical interviews.
