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Whole-language and whole word methods of instruction generally use stories with familiar high-frequency words arranged in predictable and repetitive patterns. [2] Whole-language texts have received increasing criticism for encouraging word guessing strategies instead of skilled reading. [ 3 ]
As the word frequency effect increased in both languages, total reading time decreased. In L1 (first language) there were higher skipping rates than in L2 (second language). This suggests that lower frequency words in L2 were harder to process than both high and low frequency words in L1.
Specifically, readers fixate their eyes on a word for a shorter time when the word occurs in a moderately or highly constraining context, compared to the same word in an unconstrained context. This is true regardless of the word's frequency or length. Readers are also more likely to skip over a word in a highly constraining context only. [5]
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Predictive text could allow for an entire word to be input by single keypress. Predictive text makes efficient use of fewer device keys to input writing into a text message, an e-mail, an address book, a calendar, and the like. The most widely used, general, predictive text systems are T9, iTap, eZiText, and LetterWise/WordWise. There are many ...
Keypad used by T9. T9's objective is to make it easier to enter text messages.It allows words to be formed by a single keypress for each letter, which is an improvement over the multi-tap approach used in conventional mobile phone text entry at the time, in which several letters are associated with each key, and selecting one letter often requires multiple keypresses.
The bag-of-words model (BoW) is a model of text which uses a representation of text that is based on an unordered collection (a "bag") of words. It is used in natural language processing and information retrieval (IR). It disregards word order (and thus most of syntax or grammar) but captures multiplicity.
Every column corresponds to a document, every row to a word. A cell stores the frequency of a word in a document, with dark cells indicating high word frequencies. This procedure groups documents, which use similar words, as it groups words occurring in a similar set of documents. Such groups of words are then called topics.
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