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  2. Natural language processing - Wikipedia

    en.wikipedia.org/wiki/Natural_language_processing

    Natural language processing ( NLP) is an interdisciplinary subfield of computer science and information retrieval. It is primarily concerned with giving computers the ability to support and manipulate human language. It involves processing natural language datasets, such as text corpora or speech corpora, using either rule-based or ...

  3. Bag-of-words model - Wikipedia

    en.wikipedia.org/wiki/Bag-of-words_model

    Bag-of-words model. The bag-of-words model is a model of text which uses a representation of text that is based on an unordered collection (or "bag") of words. It is used in natural language processing and information retrieval (IR). It disregards word order (and thus any non-trivial notion of grammar [clarification needed]) but captures ...

  4. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    e. Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words. The word2vec algorithm estimates these representations by modeling text in a large corpus. Once trained, such a model can detect synonymous ...

  5. BERT (language model) - Wikipedia

    en.wikipedia.org/wiki/BERT_(language_model)

    After pre-training, BERT can be fine-tuned with fewer resources on smaller datasets to optimize its performance on specific tasks such as NLP tasks (language inference, text classification) and sequence-to-sequence based language generation tasks (question-answering, conversational response generation).

  6. Word embedding - Wikipedia

    en.wikipedia.org/wiki/Word_embedding

    In natural language processing (NLP), a word embedding is a representation of a word. The embedding is used in text analysis. Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]

  7. Natural-language understanding - Wikipedia

    en.wikipedia.org/wiki/Natural-language_understanding

    Natural-language understanding. Natural-language understanding ( NLU) or natural-language interpretation ( NLI) [1] is a subset of natural-language processing in artificial intelligence that deals with machine reading comprehension. Natural-language understanding is considered an AI-hard problem. [2]

  8. Text segmentation - Wikipedia

    en.wikipedia.org/wiki/Text_segmentation

    Text segmentation is the process of dividing written text into meaningful units, such as words, sentences, or topics. The term applies both to mental processes used by humans when reading text, and to artificial processes implemented in computers, which are the subject of natural language processing. The problem is non-trivial, because while ...

  9. Textual entailment - Wikipedia

    en.wikipedia.org/wiki/Textual_entailment

    Textual entailment. In natural language processing, textual entailment ( TE ), also known as natural language inference ( NLI ), is a directional relation between text fragments. The relation holds whenever the truth of one text fragment follows from another text.