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PowerPoint 演示文稿
PowerPoint 演示文稿

... Another reason for language change is the need to create new word and expression to refer to newly developed concepts and new things. Youngsters are creative enough in the use of language. They may use a word different from that used by the older generations to refer to the same thing. For example, ...
Christiane Fellbaum, How and when to add a new concept and how
Christiane Fellbaum, How and when to add a new concept and how

... Motivated by the need to distinguish subcategories Intuitive but backed up by corpus data (e.g., classes of verb arguments) Can be semi-automatically discovered through clustering of arguments Expect some/many to be lexicalized in other languages (accidental lexical gaps in ...
November 20, 2003 Chapter 16 Lexical Semantics
November 20, 2003 Chapter 16 Lexical Semantics

... We can use more word meanings that can be explicitly listed in the lexicon. There are productive processes for creating new senses from those explicitly listed, including • Metaphor • Metonymy ...
Word, word-form, lexeme
Word, word-form, lexeme

... A sentence consists of clauses, a clause consists of one or more phrases, a phrase consists of one or more words, a word of one or more morphemes, a morpheme consists of one or more phonemes. ...
Compound nouns
Compound nouns

... is the process of forming new words by adding affixes. It is the most common word formation process to be found in the production of new English words. Some familiar examples are the elements un-, mis-, pre-, -ful, -less, -ish, -ism and -ness which appear in words like unhappy, misrepresent, prejudg ...
part of speech tagging
part of speech tagging

... How do we decide the correct POS for a word?  Syntagmatic Information: Look at tags of other words in the context of the word we are interested in.  Lexical Information: Predicting a tag based on the word concerned. For words with a number of POS, they usually occur used as one particular POS. ...
A Modern Take (Is Take a Noun?) on Parts of Speech
A Modern Take (Is Take a Noun?) on Parts of Speech

... Because these ex–parts of speech, unlike the now-parts, have only one natural form, it would make no sense to tack on a prefix or suffix to test a word for, say, prepositionness or pronounness. A given word, like from or she, might usually act as a preposition or as a pronoun, and so we often comfor ...
Spelling Punctuation and Grammar PowerPoint
Spelling Punctuation and Grammar PowerPoint

... request; go in – enter] How words are related by meaning as synonyms and antonyms [for example, big, large, little]. ...
AP English Summer Assignment File
AP English Summer Assignment File

... Agreement in direction, tendency, or character; the state or condition of being parallel. A figure of speech in which parallelism is reinforced by members that are of the same length. A well-known example of this is Julius Caesar's "Veni, vidi, vici" The placing of a sentence or one of its parts aga ...
Example - PRAXIS-Study
Example - PRAXIS-Study

... • Phonology is just one of several aspects of language. It is related to other aspects such as phonetics, morphology, syntax, and pragmatics. • Is the basis for further work in morphology, syntax, discourse, and orthography design. • Analyzes the sound patterns of a particular language by determinin ...
Lemmatization of Multi-word Lexical Units: In which Entry?
Lemmatization of Multi-word Lexical Units: In which Entry?

... idiom, and the user should still be able to find the subentry for svaret blœser i vinden in the entry svar (first noun in the group) since this element seems only to vary in number. The situation gets more difficult in the examples where the word svar is replaced by other (more or less synonymous) e ...
Presentation_Hao_Li - Programming Systems Lab
Presentation_Hao_Li - Programming Systems Lab

... consisting of an infusion of ground coffee beans” but not other meanings. And when people talks about “java”, they may talk about the beverage or the programming language “java”. ...
Resources - CSE, IIT Bombay
Resources - CSE, IIT Bombay

... Languages poor in morphology: Chinese, English Languages with rich morphology have the advantage of easier processing at higher stages of processing A task of interest to computer science: Finite State Machines for ...
Final Test - Urmila Devi Dasi
Final Test - Urmila Devi Dasi

... 2.Are you sure there are enough stamps on your package? 3.I saw him at his initiation in Philadelphia. 4.Dogs, hogs, camels, and asses cannot understand the science of God 5.They told us to set up our book table in Johnson Park. B.Copy the following sentences. Underline the ^Sconcrete^S nouns and ci ...
Document
Document

... Match the picture to the correct phrase for each pain or illness. Copy each phrase in French and English into your exercise book. ...
Narrow, Broad and Simple: What is correct practice for
Narrow, Broad and Simple: What is correct practice for

... Sense making is difficult ...
Diapositiva 1 - Roma Tre University
Diapositiva 1 - Roma Tre University

... General language texts vs special language texts General language texts: words selected meaningfully and combined logically to form everyday language discourse. Special language texts: terms=words assigned to concepts used in special languages (subject-field or domain-related texts) Terms extracted ...
Lecture 14
Lecture 14

... An adverb is a part of speech. It is any word that modifies any othe r part of language: verbs, adjectives (including numbers), clauses, sentences and other adverbs, except for nouns; modifiers of nouns are primarily determiners and adjectives. ...
THE PAPER OF LINGUISTICS “WORD
THE PAPER OF LINGUISTICS “WORD

... Around 1900, in New Berlin, Ohio, a department-store worker named, Murray Spangler invented a device which he called an electric suction sweeper. This device eventually became very popular and could have been known as a spangler. People could have been spanglering their floors or they might even hav ...
Correct Word Choice
Correct Word Choice

... Use words carefully and precisely. The following words and expressions are frequently misused. A and An. Use the article a before an initial h pronounced even slightly (a historian, a hypothesis, a horse). Use of an in such cases is considered affected or archaic in this country. Affect, effect. In ...
The language of Spoken Discourse:
The language of Spoken Discourse:

... Many verb phrases are based on and built around the most common verbs in the language: • ‘go’, ‘have’, ‘put’, ‘do’, etc. • They often combine with nouns to make common phrases – ‘have a look’, ‘go for a walk’, ‘do the washing up’, etc. Such verbs are known as delexical verbs and they are more common ...
Module 5 Academic Language Application In the Key to EAP
Module 5 Academic Language Application In the Key to EAP

... 1 The flight departs / will depart / is departing at 7 am tomorrow morning. 2 By next week, the student will have been studying / will have studied at Meldon University for three years. 3 I am deferring / going to defer my course for six months as I am overwhelmed by the workload. 4 The pensioner ju ...
Finding Semantically Related Words in Large Corpora
Finding Semantically Related Words in Large Corpora

... Partitional clustering techniques are used more frequently than hierarchical techniques in pattern recognition. However, we argue that the number of clusters in the data, their shapes and sizes, depend highly on the particular application that should benefit from the clustered data. As our aim is to ...
PSSA English Language Arts Glossary Grade 4
PSSA English Language Arts Glossary Grade 4

... subject-verb agreement - A grammatical rule in which the subject of a sentence must agree with its verb in both number and tense. subordinating conjunctions - (after, because, although) emphasize the importance of one grammatical structure over the other. summarize - To capture all of the most impor ...
Revised Language Standards
Revised Language Standards

... Demonstrate understanding of figurative language, word relationships, and nuances in word meanings. a. Interpret figures of speech (e.g., literary, biblical, and mythological allusions) in context. b. Use the relationship between particular words (e.g., synonym/antonym, analogy) to better understand ...
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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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