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(2020) use both word embeddings and masked language models to obtain augmented data. They apply the tokenizer of BERT to tokenize words into multiple word pieces. Each word piece is replaced with probability 0.4. If a word piece is not a complete word (“est” for example), it is ...
likes snow, is awake, and is in a room that looks out onto an outside area where it is snowing. From these events, it is inferred that James is no longer calm, is happy, and intends to play outside his house:
Syntactic parsing is the task of constructing a syntactic parse tree over a sentence which describes the structure of the sentence. Parse trees are used as part of many language processing applications. In this paper, we present a multi-lingual dependency parser. Using advanced deep learning ...
The transformer model is a famous natural language processing model proposed by Google in 2017. Now, with the extensive development of deep learning, many natural language processing tasks can be solved by deep learning methods. After the BERT model was
Natural language processing (NLP) enables the extraction of information embedded within unstructured texts, such as clinical case reports and trial eligibility criteria. By identifying relevant medical concepts, NLP facilitates the generation of structur
Solving combinatorial optimization problems involves a two-stage process that follows the model-and-run approach. First, a user is responsible for formulat
Natural language processing (NLP) is a field of computer science that studies how computers and humans interact. In the 1950s, Alan Turing published an article that proposed a measure of intelligence, now called the Turing test. More modern techniques, such as deep learning, have produced ...
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