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platform: Used in place of "release" if the title is not a video game, but a spin-off title. For example, anime or manga series, radio drama, expansion, etc. caption: Used to add a table caption for accessibility. If the caption would duplicate a section header, limit it to screen readers using the {} template, e.g. {{sronly|List of games}}
The following list of text-based games is not to be considered an authoritative, comprehensive listing of all such games; rather, it is intended to represent a wide range of game styles and genres presented using the text mode display and their evolution across a long period.
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]
For example, a 1984 video game and console both continue to exist as long as copies of both are in circulation, but both a canceled video game and a discontinued online game exist only in the past tense. The Nintendo Entertainment System is an 8-bit video game console, and Super Mario Bros. is a video game.
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 linguistics, center embedding is the process of embedding a phrase in the middle of another phrase of the same type. This often leads to difficulty with parsing which would be difficult to explain on grammatical grounds alone. The most frequently used example involves embedding a relative clause inside another one as in:
The title page often shows the title of the work, the person or body responsible for its intellectual content, and the imprint, which contains the name and address of the book's publisher and its date of publication. [2] Particularly in paperback editions it may contain a shorter title than the cover or lack a descriptive subtitle.
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.