Coreference resolution is a natural language processing task that identifies which expressions in a text refer to the same person, thing or matter. In “Ms Weber called. She would like a call back”, the system must understand that “she” means Ms Weber. This matters for summaries, information extraction and chatbots that need to keep track of references across several sentences or conversation turns.
Natural language processing is the branch of AI that enables computers to analyse, understand and generate human language in text or speech. It covers tasks such as translation, classification, summarisation, sentiment analysis and question answering. Rules and statistical methods once dominated; today the field is shaped by large language models.
Named entity recognition (NER) is a natural language processing technique that finds names of people, companies and places, as well as dates or amounts, in text and assigns each to a category. It turns free text into structured data. Example: the sender’s company, contact person, delivery date and invoice amount are extracted automatically from an email and transferred to a CRM.
Dependency parsing is a natural language processing technique that represents the grammatical structure of a sentence as dependencies between words. Each word is linked to a governing word, for example subject and object to the verb: in 'The customer orders two lamps', 'customer' and 'lamps' depend on 'orders'. This helps with information extraction, for instance to identify who did what.
Natural language understanding is the part of language processing that captures the meaning of an utterance: what intent lies behind it, which people, places or dates are mentioned, and what is its tone? Together with natural language generation (NLG), NLU forms one of the two sides of NLP. One example is a customer service system that recognises “My parcel still hasn't arrived” as a delivery enquiry.
