Natural Language Processing (NLP) is the branch of computer science and artificial intelligence that deals with human language. "Natural language" is contrasted with artificial languages like programming languages; it refers to languages that evolved naturally among people, such as English, Japanese, and Chinese. Search engines, voice assistants, machine translation services, chatbots, and spam filters all rely on NLP technology.

NLP processing is organized in layers. At the lowest level, morphological analysis segments text into words and assigns parts of speech. Syntactic parsing identifies grammatical relationships between words (subject-verb, modifier-modified). Semantic analysis interprets the meaning of sentences and resolves lexical ambiguity. Discourse analysis handles context that spans multiple sentences, such as resolving pronouns and identifying logical relationships between clauses. Each layer depends on the output of the layer below it, so the accuracy of morphological analysis has a cascading effect on overall quality.

Japanese NLP faces challenges that do not exist in English. First, there are no spaces between words, making morphological analysis an essential preprocessing step. Second, subjects are frequently omitted and must be inferred from context. Third, the honorific system is elaborate, with the same meaning expressed in many forms (for example, "eat" can be taberu, meshiagaru, or itadaku). Fourth, kanji readings are context-dependent (the character for "life" can be read as nama, ikiru, umareru, and more), making pronunciation estimation difficult.

Since the introduction of the Transformer architecture in 2017, NLP has advanced dramatically. BERT (2018) learned context-aware word representations, and the GPT series (2018 onward) demonstrated large-scale text generation. These large language models (LLMs) perform traditional NLP tasks such as translation, summarization, and question answering at near-human accuracy, and have expanded into areas previously outside the scope of NLP, including programming and creative writing. NLP books on Amazon provide comprehensive introductions to these developments.

NLP and character counting are closely connected. Morphological analysis directly underpins word counting. Sentence boundary detection is required for sentence counting. Reading time estimation relies on word count and sentence complexity analysis. Sentiment analysis can gauge how much emotion is conveyed within a character-limited social media post. The advanced features of a character counting tool would be impossible without NLP technology.