Following sentences in such

Xem 1-16 trên 16 kết quả Following sentences in such
  • Sandwiches arecommon in manycountries. Wheredidthis strange name come from? TheEarlof Sandwich (1718-1792) wasanEnglishman.Heliked to playcards. Onenightheplayed for hours and gotveryhungry. However,hedidn't wantto SlOphiscard game. Heasked for some roast meat betWeentWopieces ofbread. (People bakeroast meatin the ovenofastove). Heate the food while heplayed cards. Peoplegave hisname to this newkindoffood

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  • * Finish each of the following sentences in such a way that  ⇒  He  it means  exactly  the same as the sentences  printed  before  has   ........................................................ it.

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  • Finish each of the following sentences in such a way that it is as similar as possible in meaning to the sentence printed before it. 1. People say that the president wanted to be a film star.

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  • We investigate the expression of opinions about human entities in user-generated content (UGC). A set of 2,800 online news comments (8,000 sentences) was manually annotated, following a rich annotation scheme designed for this purpose. We conclude that the challenge in performing opinion mining in such type of content is correctly identifying the positive opinions, because (i) they are much less frequent than negative opinions and (ii) they are particularly exposed to verbal irony.

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  • We are concerned with grammar-based surfacesyntactic analysis of running text. Morphological and syntactic analysis is here based on tags that express surface-syntactic relations between functional categories such as Subject, Modifier, Main verb etc.; consider the following simple sentence:

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  • A text followed by eight multiple-choice questions. A text from which seven sentences have been removed and placed in a jumbled order, together with an additional sentence, after the text. A text or several short texts preceded by 15 multiplematching questions. One compulsory question. Candidates choose one task from a choice of five questions (including the set text options).

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  • AIMS To evaluate the students’ knowledge about some main contents they have learnt from U9-U11 such as : adjectives, question words, quantifiers, present and present continuos tenses… II. CONTENT A. Correct the mistakes in the following sentences a. b. c. d. My mother eats an apple

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  • Ebook Spectrum Language Arts Grade 4 includes focused practice for language arts mastery such as grammar and usage, parts of speech and sentence types, vocabulary acquisition and usage, and a writer's guide. Spectrum(R) Reading workbooks contain focused practice for reading comprehension, including letters and sounds, word recognition, integration of knowledge and ideas, key ideas and details, main idea, story structure, theme, and summarization. Each lesson features an illustrated story followed by exercise in comprehension.

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  • Imperative Complete the following sentences with the imperative form of the verb in brackets: 0. Leon, water the flowers in the evening! 1. Sam, __________________ (not to call) me before 8 o’clock! 2. Please __________________ (to feed) the dog twice a day! 3. __________________ (not to touch) the hot electric iron! 4. Hold on __________________ (to hold on) for a minute! 5. Please __________________ (to be) nice to your little sister! Complete the following sentences with the imperative form of the verb in brackets: 0. Let’s play our favourite games! 1.

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  • Following Link [14, 13] and Roberts [15], I present a semantic analysis of collective- distributivity comes from either an explicit quantifidistributive ambiguity, and resolution of such am- cational operator like each or an implicit distributive biguity by model-based reasoning. This approach operator called the D o p e r a t o r . The D operator goes beyond Scha and Stallard [17], whose reasoning was motivated by the equivalence in the semantics capability was limited to checking semantic types. of the following sentences.

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  • Though most text generators are capable of simply stringing together more than one sentence, they cannot determine which order will ensure a coherent paragraph. A paragraph is coherent when the information in successive sentences follows some pattern of inference or of knowledge with which the hearer is familiar. To signal such inferences, speakers usually use relations that llnk successive sentences in fixed ways. A set of 20 relations that span most of what people usually say in English is proposed in the Rhetorical Structure Theory of M a n n and Thompson.

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  • 31 suppose that it has the function of a preclausal determiner (i.e. a determiner introducing the following italicised clause Randy Rabbit runs Benny’s Bunny Bar) in sentences such as (49b). However, there is evidence against a determiner analysis of the complementiser that. Part of this is phonological in nature. In its use as a complementiser (in sentences such as (49b) above), that typically has the reduced form /ð¶t/, whereas in its use as a determiner (e.g.

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  • Mining bilingual data (including bilingual sentences and terms1) from the Web can benefit many NLP applications, such as machine translation and cross language information retrieval. In this paper, based on the observation that bilingual data in many web pages appear collectively following similar patterns, an adaptive pattern-based bilingual data mining method is proposed.

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  • Certain restrictions on possible scopings of quantifiednoun phrases in natural language are usually expressed in terms of formal constraints on binding at a level of logical form. Such reliance on the form rather than the content of semantic interpretations goes against the spirit of compositionality. I will show that those scoping restrictions follow from simple and fundamental facts about functional application and abstraction, and can be expressed as constraints on the derivation of possible meanings for sentences rather than constraints of the alleged forms of those meanings. ...

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  • IN THE NEAR FUTURE an attempt to translate from a foreign language by machine will be made at the computational laboratory of Birkbeck College. It will differ from previous experiments in that the sentences or passages of French to be translated will not have been specially chosen or "doctored" in any way beforehand: on the contrary, they will be constructed by French scholars, who will be invited to do their best to fault the machine. What follows is an account of the method, or program, which makes such an experiment possible. ...

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  • We have developed an automated Japanese essay scoring system called Jess. The system needs expert writings rather than expert raters to build the evaluation model. By detecting statistical outliers of predetermined aimed essay features compared with many professional writings for each prompt, our system can evaluate essays.

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