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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]
fastText is a library for learning of word embeddings and text classification created by Facebook's AI Research ... Several papers describe the techniques used by ...
T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. [ 1 ] [ 2 ] Like the original Transformer model, [ 3 ] T5 models are encoder-decoder Transformers , where the encoder processes the input text, and the decoder generates the output text.
Word2vec was created, patented, [7] and published in 2013 by a team of researchers led by Mikolov at Google over two papers. [1] [2] The original paper was rejected by reviewers for ICLR conference 2013. It also took months for the code to be approved for open-sourcing. [8] Other researchers helped analyse and explain the algorithm. [4]
In 2013 DiGRA launched the journal Transactions of Digital Games Research Association (ToDiGRA). The journal is refereed, open access, and dedicated to furthering the aims of the organization by disseminating "the wide variety of research within the game studies community combining, for example, humane science with sociology, technology with design, and empirics with theory". [5]
Power Factor has been cited as a rare example of a video game in which the entire concept is a video game within a video game: The player takes on the role of a character who is playing a "Virtual Reality Simulator", in which he in turn takes on the role of the hero Redd Ace. [23] The .hack franchise also gives the concept a central role. It ...
This category includes individuals who have contributed significant academic research, scholarly commentary, or journalistic coverage on the topic of games. Pages in category "Video game researchers" The following 36 pages are in this category, out of 36 total.
In practice however, BERT's sentence embedding with the [CLS] token achieves poor performance, often worse than simply averaging non-contextual word embeddings. SBERT later achieved superior sentence embedding performance [8] by fine tuning BERT's [CLS] token embeddings through the usage of a siamese neural network architecture on the SNLI dataset.