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  2. Encoding/decoding model of communication - Wikipedia

    en.wikipedia.org/wiki/Encoding/decoding_model_of...

    In the process of encoding, the sender (i.e. encoder) uses verbal (e.g. words, signs, images, video) and non-verbal (e.g. body language, hand gestures, face expressions) symbols for which he or she believes the receiver (that is, the decoder) will understand. The symbols can be words and numbers, images, face expressions, signals and/or actions.

  3. Sentence embedding - Wikipedia

    en.wikipedia.org/wiki/Sentence_embedding

    BERT pioneered an approach involving the use of a dedicated [CLS] token prepended to the beginning of each sentence inputted into the model; the final hidden state vector of this token encodes information about the sentence and can be fine-tuned for use in sentence classification tasks. In practice however, BERT's sentence embedding with the ...

  4. Encoding (memory) - Wikipedia

    en.wikipedia.org/wiki/Encoding_(memory)

    This allows data to be conveyed in the short term, without consolidating anything for permanent storage. From here a memory or an association may be chosen to become a long-term memory, or forgotten as the synaptic connections eventually weaken. The switch from short to long-term is the same concerning both implicit memory and explicit memory.

  5. Source–message–channel–receiver model of communication

    en.wikipedia.org/wiki/Source–message–channel...

    The channel is the means used to send the message. The receiver is the audience for whom the message is intended. They have to decode it to understand it. [4] [30] Despite the emphasis on only four basic components, Berlo initially identifies a total of six components. The two additional components are encoder and decoder. [31]

  6. BERT (language model) - Wikipedia

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

    High-level schematic diagram of BERT. It takes in a text, tokenizes it into a sequence of tokens, add in optional special tokens, and apply a Transformer encoder. The hidden states of the last layer can then be used as contextual word embeddings. BERT is an "encoder-only" transformer architecture. At a high level, BERT consists of 4 modules:

  7. Word embedding - Wikipedia

    en.wikipedia.org/wiki/Word_embedding

    In natural language processing, 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]

  8. Context-dependent memory - Wikipedia

    en.wikipedia.org/wiki/Context-dependent_memory

    In psychology, context-dependent memory is the improved recall of specific episodes or information when the context present at encoding and retrieval are the same. In a simpler manner, "when events are represented in memory, contextual information is stored along with memory targets; the context can therefore cue memories containing that contextual information". [1]

  9. Sentence completion tests - Wikipedia

    en.wikipedia.org/wiki/Sentence_completion_tests

    Sentence completion tests typically provide respondents with beginnings of sentences, referred to as "stems", and respondents then complete the sentences in ways that are meaningful to them. The responses are believed to provide indications of attitudes , beliefs , motivations , or other mental states .