Nlp

• Word Embedding

  Dense vector representation of a word in low-dimensional space, capturing semantic and syntactic relationships.

• Text Classification

  Assigning one or more categories to text; includes sentiment analysis, topic classification, spam detection, and intent recognition.

• Sequence-to-Sequence

  Encoder-decoder architecture mapping input sequences to output sequences; used for translation, summarisation, and dialogue.

• Sentence Boundary Detection

  Identifying sentence boundaries in text; handles ambiguous punctuation (periods in abbreviations, decimal points, URLs) and enables sentence-level processing.

• RLHF

  Reinforcement Learning from Human Feedback; uses human preference comparisons to fine-tune language models for safety and alignment.

• Part-of-Speech Tagging

  Assigning grammatical roles (noun, verb, adjective, etc.) to tokens in text; fundamental for syntax analysis and downstream NLP tasks.

• Named Entity Recognition

  Identifying and classifying named entities (persons, locations, organisations) in text; fundamental NLP task for information extraction.

• Language Model

  Probability distribution over sequences of tokens; predicts next token given context. Foundation of NLP from n-grams to large language models.

• Instruction Tuning

  Fine-tuning language models on diverse (instruction, response) pairs to improve generalization and follow natural language instructions.

• Dependency Parsing

  Analysing grammatical structure by identifying directed dependency relations between tokens; output is a dependency tree.

• Coreference Resolution

  Linking mentions of the same entity across a document; resolves pronouns and nominal references to their antecedents.

• Cloze Task

  Predicting masked tokens from context; unsupervised pre-training objective where random words are hidden and must be inferred.

• Chunking

  Grouping tokens into phrases or chunks; shallow syntactic analysis that segments noun phrases, verb phrases, and prepositional phrases.