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A weighted context-free grammar (WCFG) is a more general category of context-free grammar, where each production has a numeric weight associated with it. The weight of a specific parse tree in a WCFG is the product [7] (or sum [8]) of all rule weights in the tree. Each rule weight is included as often as the rule is used in the tree.
In practice it was necessary to smooth the probability distributions by also assigning non-zero probabilities to unseen words or n-grams. The reason is that models derived directly from the n-gram frequency counts have severe problems when confronted with any n-grams that have not explicitly been seen before – the zero-frequency problem.
In linguistics, it is considered important to distinguish errors from mistakes. A distinction is always made between errors and mistakes where the former is defined as resulting from a learner's lack of proper grammatical knowledge, whilst the latter as a failure to use a known system correctly. [9]
It disregards word order (and thus most of syntax or grammar) but captures multiplicity. The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a feature for training a classifier. [1] It has also been used for computer vision. [2]
In order to be able to meticulously study the English language, an annotated text corpus was much needed. The Penn Treebank [ 5 ] was one of the most used corpora. It consisted of IBM computer manuals, transcribed telephone conversations, and other texts, together containing over 4.5 million words of American English, annotated using both part ...
In some dialects of spoken English, of and the contracted form of have, 've, sound alike. However, in standard written English, they are not interchangeable. Standard: Susan would have stopped to eat, but she was running late. Standard: You could have warned me! Non-standard: I should of known that the store would be closed. (Should be "I ...
A chart showing a uniform distribution. In probability theory and statistics, a collection of random variables is independent and identically distributed (i.i.d., iid, or IID) if each random variable has the same probability distribution as the others and all are mutually independent. [1]
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