Natural Language Processing (NLP) encompasses the set of methods and technologies that enable machines to understand, generate, interpret, or manipulate human language in textual or spoken form. At the intersection of linguistics, computer science, and artificial intelligence, NLP seeks to equip computers with linguistic capabilities while addressing the complexity, ambiguity, and richness of natural languages. This field contrasts with systems handling formal languages (such as programming languages), as NLP must manage semantic ambiguity, polysemy, variable syntax, and the implicit contextualization inherent to human language.
Use Cases and Examples
NLP is applied in many areas: sentiment analysis on social media, machine translation (Google Translate, DeepL), text generation (chatbots, voice assistants), automatic document summarization, information extraction (search engines, automated monitoring), grammar correction, speech recognition, and speech synthesis.
For example, automated email response systems use NLP to understand message content and suggest appropriate replies. Companies use NLP to analyze customer feedback and detect emerging trends or issues.
Main Software Tools, Libraries, Frameworks
Several frameworks and libraries are widely used in NLP. Key examples include NLTK (Python), spaCy, Stanford NLP, OpenNLP, and CoreNLP. For deep learning, Transformers (Hugging Face), Fairseq (Facebook), BERT, GPT, and T5 are prominent architectures and tools. Speech recognition often uses Kaldi, DeepSpeech, or Wav2Vec. For machine translation, MarianNMT and OpenNMT are used.
Recent Developments, Evolutions, and Trends
Recent major advances rely on large language models (LLMs), such as GPT-4, BERT, or LLaMA, capable of handling complex tasks in understanding, generating, and translating text at near-human levels. Trends include the integration of NLP into multimodal systems (text, image, audio), optimizing models for resource efficiency, and improving robustness against linguistic bias and errors. Additionally, tailoring NLP to specific contexts or domains (healthcare, law, finance) is becoming increasingly common.