Natural Language Processing Learning Path

Definition and scope of natural language processing

Natural language processing (NLP) is the field of computer science focused on enabling computers to process natural language information. It is closely connected to artificial intelligence and also draws on related areas such as information retrieval, knowledge representation, computational linguistics, and broader linguistics. Core NLP processing tasks include speech recognition, text classification, natural language understanding, and natural language generation. The scope of NLP covers both the analysis of language (e.g., turning raw text or speech into structured representations) and the generation of language (producing text or speech). Common NLP tasks span multiple levels of linguistic processing, including text/speech processing (such as OCR and speech recognition), morphological analysis (lemmatization, stemming, and segmentation), syntactic analysis (e.g., sentence boundary detection and grammar-related tasks), and semantics (lexical, relational, and discourse-level meaning). NLP also includes higher-level applications that use these subtasks to solve real-world problems. NLP is typically approached using different paradigms—symbolic (rule-based), statistical (machine learning using probabilistic models), and neural network methods (deep learning). Over time, the field has shifted from hand-crafted rules toward data-driven learning, with neural methods increasingly dominating modern systems due to their ability to learn representations and achieve state-of-the-art performance across many language tasks.

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Major NLP processing tasks

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