LEXICAL CONTEXT PRIMING ABSTRACTION

RAISINS, SULTANAS~ AND CURRANTS: LEXICAL CLASSIFICATION AND ABSTRACTION VIA CONTEXT PRIMING David J. Hutches Department of Computer Science and Engineering, Mail Code 0114 University of California, San Diego La Jolla, CA 92093-0114 dhutches@ucsd.edu Abstract In this paper[r]

tions of edges in the network to compute the similarity of two nodes. He then exploited the moving fixed size window to minimize the sum 929of all combinations of the shortest distances among target and context words. Pedersen et al. (2003) extended Lesk’s defini-tion method (1986) to discrim[r]

This book systematically teaches grammar and vocabulary and helps students develop all the skills necessary to succeed in all exams at B2 level.
Presentation of lexical items and grammatical structures in context (including collocations, expressions, phrasal verbs, words with prepositions, prepositi[r]

{magnate → king} {magnate → businessman}OUTPUT: In 1900, Omaha was the center of a nationaluproar over the kidnapping of Edward Cudahy, Jr., theson of a local meatpacking businessman.Figure 1: Input sentence, candidate simplification rules,and output sentence.consumers looking for medical information[r]

ated triplets to capture long-distance dependenciesthat go beyond the local context of phrases, and thelatter built the classifiers which combine rich con-text information to better select translation duringdecoding. With the consideration of various localcontext features, these approaches all[r]

I. Objective:
By the end of the lesson, Students will be able to:
Develop such reading skills as understanding main ideas, scanning for specific information, guessing meaning in context, and summarizing.
Use the information they have read to discuss the advantages and disadvantages of different so[r]

scalars, which requires the definition of explicit addressing schemes. Thiscorresponds to the transaction accurate architecture level APIs.The Multiflex approach proposed in [10] targets multimedia and net-working applications, with the objective of having good performance evenfor small granularity[r]

Choosing the Word Most Typical in Context Using a Lexical Co-occurrence Network Philip Edmonds Department of Computer Science, University of Toronto Toronto, Canada, M5S 3G4 pedmonds©cs, toronto, edu Abstract This paper presents a partial solution to a com- ponent of the problem of [r]

categorized as a drug and thus be considered moresimilar to heroin. Look, for instance, at the simi-lar ways in which these words can be used: one canbe ”chocolate-crazed” or ”chocolate-addicted” andsuffer ”chocolate-induced” symptoms (e.g., each ofthese uses can be found in the pages of Wikipedia).[r]

Period: 3 UNIT 1: My hobbies
Lesson 2: A closer look 1

A. Objectives.
By the end of the lesson, Ss will be able to use lexical items related to hobbies. Pronounce the sounds and ɜ : in context.
B. Language knowledge
1. Vocabulary: Hobbies and action verbs
2. Grammar: The simp[r]

PARALLEL MULTIPLE CONTEXT-FREE GRAMMARS, FINITE-STATE TRANSLATION SYSTEMS, AND POLYNOMIAL-TIME RECOGNIZABLE SUBCLASSES OF LEXICAL-FUNCTIONAL GRAMMARS Hiroyuki Seki tt Ryuichi Nakanishi t Yuichi Kaji t Sachiko Ando t Tadao Kasami $t t Department of Information and Computer Sciences, Fac[r]

the computer tutor differently and perhaps more id-iosyncratically than to the human tutor, 3) the com-puter tutor is less “flexible” than the human tutor(allowing little student initiative, questions, ground-ings, contextual references, etc.), which also effectsstudent emotional response and its exp[r]

lexical only 64.4combination+lexical (T&C08) 65.2lexical+parse 68.1all features (+Parse) 68.5Table 2: Accuracy on preposition selection taskfor various feature combinationsThe Preposition Head and Complement Mixedfeatures are created by taking the first feature inthe prev[r]

placeasinreal-storekernels.Virtual storage introduces a number of simplifications into a kernel but atthe expense of a more complex bootstrap and a more involved storage man-ager (in particular, it needs to be optimised more carefully, a point discussedin some detail in Chapter 6). Virtual machines a[r]

on the desk?Phong: Sure. Do you want to check the TVschedule?Hung: Yes. Look ….2EMHFWLYHVBy the end of this unit, Ss can:•••••pronounce the sounds /θ/ and /ð/ correctly in isolation and in contextuse the lexical items related to televisionuse conjunctions (and, but, because…) and question wor[r]

1 IntroductionSignificant progress has been made in statisti-cal machine translation (SMT) in recent years.Among all the proposed approaches, the phrase-based method (Koehn et al., 2003) has become thewidely adopted one in SMT due to its capabilityof capturing local context information from ad[r]

Unit 7: POLUTIONObjectives:By the end of this unit, students can:pronounce the words ending in -ic and -al correctly in isolation and in contextuse lexical items related to the topic ‘Pollution’ to talk about types of pollutionuse words and phrases showing cause/ effect relationships to descr[r]

Quantifying lexical influence: Giving direction to context V Krip~sundar kripa~cs, buffalo, edu CEDAR & Dept. of Computer Science SUNY at Buffalo Buffalo NY 14260, USA Abstract The relevance of context in disambiguat- ing natural language input has been widely acknowledg[r]

discriminative, feature-based predicate translationmodel that captures not only lexical information(i.e., surrounding words) but also high-level seman-tic contexts to correctly translate predicates.Arguments contain information for questions ofwho, what, when, where, why, and how in sentences[r]

chain is removed. A popular graph clustering algorithm, Markov Clustering (MCL) is based on the idea that “a random walk that visits a dense cluster will likely not leave the cluster until many of its vertices have been visited” (van Dongen, 2000). MCL isimplemented as a sequence of iterative operat[r]