Xem 1-20 trên 20 kết quả Words are categorical
  • Brian P. Cleary is the creator of the best-selling Words Are CATegorical(tm) series, now a 13-volume set with more than 2 million copies in print. He is also the author of the Math Is CATegorical(tm) series and the single titles Rainbow Soup: Adventures in Poetry, Rhyme and PUNishment: Adventures in Wordplay, Eight Wild Nights: A Family Hanukkah Tale, Peanut Butter and Jellyfishes: A Very Silly Alphabet Book and The Laugh Stand: Adventures in Humor. Mr. Cleary lives in Cleveland, Ohio.

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  • In this fun-filled book, playful puns and comical cartoon cats combine to show, not tell, readers what prepositions are all about. Each preposition in the text, like under, over, by the clover, about, throughout, and next to Rover, is highlighted in color for easy identification. This is the newest addition to the Words Are CATagorical(tm) series, which has sold over 450,000 copies.

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  • "Word-Nerd," Brian P. Cleary and highly-acclaimed illustrator, Brian Gable collaborate to clarify the concept of synonyms for young readers with playful, lively, and whimsical rhymes and humorous, comical, and amusing illustrations. For easy identification, synonyms are printed in color, and key words are illustrated on each page. This funny, best-selling series shows, not tells, each part of speech.

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  • Tham khảo sách 'a mink, a fink, a skating rink: what is a noun words are categorical', tài liệu phổ thông, tiếng anh phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả

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  • Brian P. Cleary is the author of the Math Is CATegorical ® series, the Adventures in Memory(tm) series, the Sounds Like Reading(tm) series, and the best-selling Words Are CATegorical ® series, including Stop and Go, Yes and No: What Is an Antonym?, How Much Can a Bare Bear Bear?: What Are Homonyms and Homophones?, and To Root, to Toot, to Parachute: What Is a Verb? He is also the author of Rainbow Soup

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  • Kindergarten-Grade 2–The team behind the Words Are CATegorical series (Carolrhoda) offers the first in a series about math. Through playful rhymes, the book explains basic concepts such as, No amount gets smaller when you're working in addition. The numbers climb from low to high 'cause that's addition's mission! Children count bubbles, rings, school buses, baseballs, baby-sitters, eggs, and musicians in this fun introduction, which also covers terms that are indicative of the operation

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  • Ready to laugh until you're horse? Brian P. Cleary, author of the best-selling Words Are CATegorical(tm) series and the poetry collection Rainbow Soup, is off on a gnu hilarious adventure in language--PUNS! This imaginative collection of silly and sophisticated puns uncovers double meanings that are kind dove hiding in everyday phrases. A helpful pun-unciation guide is included on porpoise to help ewe give it a try!

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  • Most text message normalization approaches are based on supervised learning and rely on human labeled training data. In addition, the nonstandard words are often categorized into different types and specific models are designed to tackle each type. In this paper, we propose a unified letter transformation approach that requires neither pre-categorization nor human supervision.

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  • This paper presents an approach to text categorization that i) uses no machine learning and ii) reacts on-the-fly to unknown words. These features are important for categorizing Blog articles, which are updated on a daily basis and filled with newly coined words. We categorize 600 Blog articles into 12 domains. As a result, our categorization method achieved an accuracy of 94.0% (564/600).

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  • Chuckle your way through the stand’s cartoons and wacky headlines and you’ll come to the grams department—a full selection of pangrams, anagrams, and even word diagrams. And the best news of all is that THE LAUGH STAND comes with a guarantee—to make you laugh.

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  • In this humorous look at length, Brian P. Cleary and Brian Gable introduce essential units—from millimeters to yards. The comical cats of the wildly popular Words Are CATegorical™ series show how to measure length, width, and height. Peppy rhyme, goofy illustrations, and kid-friendly examples make short work of this tall topic. Ages 5-8 Reinforced binding To Renee —B.P.C. Length: e The distancnd from one eng of somethi er to the oth A short note about length: People in the United States and Canada have two systems to measure length. One is called the English system, or the U.

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  • Words and character-bigrams are both used as features in Chinese text processing tasks, but no systematic comparison or analysis of their values as features for Chinese text categorization has been reported heretofore.

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  • The API computes semantic relatedness by: 1. taking a pair of words as input; 2. retrieving the Wikipedia articles they refer to (via a disambiguation strategy based on the link structure of the articles); 3. computing paths in the Wikipedia categorization graph between the categories the articles are assigned to; 4. returning as output the set of paths found, scored according to some measure definition. The implementation includes path-length (Rada et al., 1989; Wu & Palmer, 1994; Leacock & Chodorow, 1998), information-content (Resnik, 1995; Seco et al.

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  • Automatic detection of general relations between short texts is a complex task that cannot be carried out only relying on language models and bag-of-words. Therefore, learning methods to exploit syntax and semantics are required. In this paper, we present a new kernel for the representation of shallow semantic information along with a comprehensive study on kernel methods for the exploitation of syntactic/semantic structures for short text pair categorization.

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  • This paper presents a study on if and how automatically extracted keywords can be used to improve text categorization. In summary we show that a higher performance — as measured by micro-averaged F-measure on a standard text categorization collection — is achieved when the full-text representation is combined with the automatically extracted keywords. The combination is obtained by giving higher weights to words in the full-texts that are also extracted as keywords.

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  • Of course, as is true for the sense of smell, a judge’s ability to taste substances in beer is useless unless that judge can accurately identify the substance and use appropriate vocabulary to communicate that information to a brewer. Meilgaard’s (1993) categorization system for beer flavors includes 6 general categories (fullness, mouthfeel, bitter, salt, sweet, and sour) consisting of 14 flavors that may be present in beer.

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  • This paper proposes the use of local histograms (LH) over character n-grams for authorship attribution (AA). LHs are enriched histogram representations that preserve sequential information in documents; they have been successfully used for text categorization and document visualization using word histograms. In this work we explore the suitability of LHs over n-grams at the character-level for AA. We show that LHs are particularly helpful for AA, because they provide useful information for uncovering, to some extent, the writing style of authors.

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  • A central issue in work on modifier ordering is how to order modifiers that are unobserved during system development. English has upwards of 200,000 words, with over 50,000 words in the vocabulary of an educated adult (Aitchison, 2003). Up to a quarter of these words may be adjectives, which poses a significant problem for any system that attempts to categorize English adjectives in ways that are useful for an ordering task. Extensive in-context observation of adjectives and other modifiers is required to 1 Introduction adequately characterize their behavior. ...

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  • In this work, we provide an empirical analysis of differences in word use between genders in telephone conversations, which complements the considerable body of work in sociolinguistics concerned with gender linguistic differences. Experiments are performed on a large speech corpus of roughly 12000 conversations. We employ machine learning techniques to automatically categorize the gender of each speaker given only the transcript of his/her speech, achieving 92% accuracy. An analysis of the most characteristic words for each gender is also presented.

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  • Chapter 1A - Introducing computer systems. This chapter includes contents: In basic terms, define the word com puter; discuss various ways computers can be categorized; identify six types of computers designed for individual use; identify four types of computers used primarily by organizations; explain the importance of computers in today's society; describe how computers are used in various sectors of our society.

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