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The Peabody Picture Vocabulary Test, the 2007 edition of which is known as the PPVT-IV, is an untimed test of receptive vocabulary for Standard American English and is intended to provide a quick estimate of the examinee's receptive vocabulary ability. It can be used with the Expressive Vocabulary Test-Second Edition (EVT-2) to make a direct ...
Reading comprehension and vocabulary are inextricably linked together. The ability to decode or identify and pronounce words is self-evidently important, but knowing what the words mean has a major and direct effect on knowing what any specific passage means while skimming a reading material.
Sansei (三世, "third generation") is a Japanese and North American English term [1] used in parts of the world (mainly in South America and North America) to refer to the children of children born to ethnically Japanese emigrants in a new country of residence, outside of Japan.
The basic reading activity involves students viewing a short subtitled presentation projected onscreen, while completing a response worksheet. To be really effective, the subtitling should have high quality synchronization of audio and text, and better yet, subtitling should change color in syllabic synchronization to audio model, and the text ...
Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.
Sequencing technologies with a different approach than second-generation platforms were first described as "third-generation" in 2008–2009. [4]There are several companies currently at the heart of third generation sequencing technology development, namely, Pacific Biosciences, Oxford Nanopore Technology, Quantapore (CA-USA), and Stratos (WA-USA).
Its vocabulary size is 30,000, and any token not appearing in its vocabulary is replaced by [UNK] ("unknown"). The three kinds of embedding used by BERT: token type, position, and segment type. The first layer is the embedding layer, which contains three components: token type embeddings, position embeddings, and segment type embeddings.
This page was last edited on 23 October 2014, at 15:16 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.
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