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Государственный экзамен ООЗО 2015 Теория первого
Государственный экзамен ООЗО 2015 Теория первого

... «I’m his mother. My son is not in town; but I daresay he will be before long. Can I give him any message?» «Well, thanks! I want to see him. I’ll ring up again; or take my chance later. Thanks!» Winifred replaced the receiver. Stainford! The voice was distinguished. She hoped it had nothing to do wi ...
Here - WordPress.com
Here - WordPress.com

... person who does what the root says, such as inventor). Other suffixes change a word’s part of speech.  For example, by adding a suffix, the verb argue can be ...
Standards Reading Specifications Phonemic Awareness Phonics
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... Context clues: Students use associated words, setting, and other text features to derive meaning and understand unknown words. Before Reading: Teachers have students preview new vocabulary works, pictures, headings, and subheadings. Students and teachers ...
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... digital technologies to be taught explicitly will ensure students are given explicit instruction in all areas. Rule based approach has been adopted (this consist of a new rule as well as revision of rule for 2 years prior to grade level) as well as high frequency, challenging words to ensure vocabul ...
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... units in phrasal verbs “get by”, “look back”, etc.  Auxiliary: do, have  Modal verbs: can, will, may, must, etc. ...
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... elements into a word, although there may be several parts of it. For example, disappointment is composed of dis + appoint + ment, but we cannot insert any letter between the different parts. • However, this feature of word is only a relative notion, and there are words that can be interrupted. For e ...
here - Teaching and Training Pathways
here - Teaching and Training Pathways

... A learning difficulty that primarily affects the skills involved in accurate and fluent reading and spelling. An element used in writing to prevent repetition, and in speech frequently involves the omission of personal subjects. For example: John: ‘Where are you going?’ Ann: ‘Up town.’ (‘Up town’ do ...
Rule Based Approach for Arabic Part of Speech Tagging and Name
Rule Based Approach for Arabic Part of Speech Tagging and Name

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Apart from conversion of word class, we have also come across a
Apart from conversion of word class, we have also come across a

... lexemes instead of nonce formations, since these words are not likely to become a permanent part of the lexicon. For example, the adjective show-businessy is a nonce formation being used in the sentence The atmosphere at the ceremony was pretty show-businessy; eelionnaire is another example of nonce ...
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Aligning words in English-Hindi parallel corpora
Aligning words in English-Hindi parallel corpora

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... the expressions are words over V ∪F , and α, β ∈ F . Since the two connectives are the same, they have the same arity: m = n, and we have αp1 · · · pn = αq1 · · · qn . If n = 0, then we are done. So assume n > 0. Then p1 · · · pn = q1 · · · qn . We want to show that pn = qn , and therefore p1 · · · ...
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... grammatical contrasts such as: Kataba  wrote, kutiba  has been written, aktub  I write/am writing. One way of representing these facts is by assigning vowels to a different tier, level : Af ...
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Chapter 2: Words, sentences, and syntax
Chapter 2: Words, sentences, and syntax

... if your purpose is to paint a nature morte, you might make colour the relevant criterion. If on the other hand you are a keeper in the monkey house of a ZOO, needing to feed your inmates, you might make weight the relevant criterion, and so on. And notice that only a combination of criteria (eg. col ...
Lesson 1 - Home2Teach.com
Lesson 1 - Home2Teach.com

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... 8 Find the mistakes in this passage. Write out the corrected passage. Suddenly, the ship swaied from side to side, battleing the enormous waves. The couragous captain controled the sails and the crew rallyed together to bail out the water. The choppyness of the water worsened and the boat tiped over ...
Morphology - Oral Language and Literacy
Morphology - Oral Language and Literacy

... Carlisle, J.F. (2010). Effects of instruction in morphological awareness on literacy achievement: An integrated review. Reading Research Quarterly, 45(4), 464–487. Carlisle, J. (2004). Morphological processes that influence learning to read. In C. A. Stone, E. R. Silliman, B. J. Ehren & K. Apel (Eds ...
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Word-sense disambiguation

In computational linguistics, word-sense disambiguation (WSD) is an open problem of natural language processing and ontology. WSD is identifying which sense of a word (i.e. meaning) is used in a sentence, when the word has multiple meanings. The solution to this problem impacts other computer-related writing, such as discourse, improving relevance of search engines, anaphora resolution, coherence, inference et cetera.The human brain is quite proficient at word-sense disambiguation. The fact that natural language is formed in a way that requires so much of it is a reflection of that neurologic reality. In other words, human language developed in a way that reflects (and also has helped to shape) the innate ability provided by the brain's neural networks. In computer science and the information technology that it enables, it has been a long-term challenge to develop the ability in computers to do natural language processing and machine learning.To date, a rich variety of techniques have been researched, from dictionary-based methods that use the knowledge encoded in lexical resources, to supervised machine learning methods in which a classifier is trained for each distinct word on a corpus of manually sense-annotated examples, to completely unsupervised methods that cluster occurrences of words, thereby inducing word senses. Among these, supervised learning approaches have been the most successful algorithms to date.Current accuracy is difficult to state without a host of caveats. In English, accuracy at the coarse-grained (homograph) level is routinely above 90%, with some methods on particular homographs achieving over 96%. On finer-grained sense distinctions, top accuracies from 59.1% to 69.0% have been reported in recent evaluation exercises (SemEval-2007, Senseval-2), where the baseline accuracy of the simplest possible algorithm of always choosing the most frequent sense was 51.4% and 57%, respectively.
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