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The standard 'vanilla' approach to locate the end of a sentence: [clarification needed] (a) If it is a period, it ends a sentence. (b) If the preceding token is in the hand-compiled list of abbreviations, then it does not end a sentence. (c) If the next token is capitalized, then it ends a sentence. This strategy gets about 95% of sentences ...
A statistical machine learning algorithm to detect whether a sentence contained a "That's what she said" double entendre was developed by Kiddon and Brun (2011). [9] There is an open-source Python implementation of Kiddon & Brun's TWSS system. [10] A program to recognize knock-knock jokes was reported by Taylor and Mazlack. [11]
GPTZero uses qualities it terms perplexity and burstiness to attempt determining if a passage was written by a AI. [14] According to the company, perplexity is how random the text in the sentence is, and whether the way the sentence is constructed is unusual or "surprising" for the application.
Artificial intelligence detection software aims to determine whether some content (text, image, video or audio) was generated using artificial intelligence (AI).. However, the reliability of such software is a topic of debate, [1] and there are concerns about the potential misapplication of AI detection software by educators.
Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence. Word2vec was developed by Tomáš Mikolov and colleagues at Google and published in 2013. Word2vec represents a word as a high-dimension vector of numbers which capture relationships between words.
The seq2seq method developed in the early 2010s uses two neural networks: an encoder network converts an input sentence into numerical vectors, and a decoder network converts those vectors to sentences in the target language. The Attention mechanism was grafted onto this structure in 2014 and shown below.