USERS NATURAL LANGUAGE EXPRESSION in Java

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USERS NATURAL LANGUAGE EXPRESSION
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Tr an sc rip t
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Ontology (Domain Concepts)
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Parse (Synta) Extract Structure Analyze Structure
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Grammar, Lexicon Structure Models Feature Models
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Continuous Words Isolated Small large Vocabulary Dependent Soeaker Independent
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FIGURE 10-1
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Natural language processing.
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Comprehensive standard de nitions are found in WordNet [233, 234], a collection of hundreds of thousands of words of English and other languages that emphasizes the psycholinguistics semantics and semantically related structures rather than just the forms (e.g., spelling and pronunciation). WordNet is incorporated by reference into RXML for lexical and ontological structure. Fundamental differences in the semantic organization of syntactic categories like noun, verb, adjective, and adverb can be seen in WordNet. According to Miller et al. [235], nouns are organized in human lexical memory as topical hierarchies. The WordNet nouns begin with the entry Entity, from the WordNet perspective, the common root of all nouns: n [noun] 01 entity | that which is perceived or known or inferred to have its own distinct existence (living or nonliving) Associated with Entity and immediately following entity are the related nouns: thing de ned as a separate and self-contained entity ; anything | a thing of any kind (e.g., Do you have anything to declare ); something | a thing of some kind (e.g., Is there something you want ); nothing | a nonexistent thing ; whole | an assemblage of parts that is regarded as a single entity (e.g., How big is that part compared to the whole ); and living_ thing | a living (or once living) entity. The numerical references establish associative links between Entity and other nouns. The companion CD-ROM follows the threads more deeply.
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USER-DOMAIN KNOWLEDGE
WordNet doesn t differentiate well between subsets (e.g., action as a type of act), synonyms (e.g., property and attribute in object-oriented programming), and related abstractions that do not necessarily share set/subset relationships but that are not synonyms in many instances (e.g., feeling and emotion). The RXML adaptation of the candidate primes leverages the insights of WordNet, adapting them to the <User/> domain. Verbs, on the other hand, may be organized in human memory by a variety of entailment relations. One might like to nd a single root verb from which all verbs might be derived in the same sense that all WordNet nouns share Entity as a root. Because of the complexity of <Action/> as a multirooted heterarchy, no single abstraction appears at the root of WordNet verbs. Instead, the WordNet types of verb <Action/> are: bodily care and functions, change, cognition, communication, competition, consumption, contact, creation, emotion, motion, perception, possession, social interaction, and weather, as well as verbs referring to states, such as suf ce, belong, and resemble, that could not be integrated into the other categories. Radio XML adopts these as the top-level categories of action in the user domain, leveraging for the <User/> the best insights of linguists into how people communicate with each other. In the psycholinguistic view, adjectives and adverbs may be organized as N-dimensional hyperspaces. Again, according to Miller, these lexical structures re ect a different way of categorizing experience. Attempts to impose a single organizing principle on all syntactic categories would badly misrepresent the psychological complexity of lexical knowledge. WordNet organizes lexical information by word meanings, rather than word forms, resembling a thesaurus more than a dictionary. WordNet adjective, adverb, noun, and verb databases for AAAI 2004 [234] included the four base les for adjectives, adverbs, verbs, and nouns consisting of 21 MB of text. Since word-level semantics are context dependent, WordNet 2.0 includes indices for clusters, similarity metrics, and related tools. The associative-semantics tools of WordNet 2.0 are incorporated into RXML by reference for <User>-domain skills for AACR. Space required for the storage of these word-level semantics along with a rich set of semantic relationships even in a handset is not excessive by today s standards. CRs also learn user-speci c word associations from the <User/> acting normally in <Scenes/> such as work, home, and leisure. WordNet provides a starting point for understanding user expressions for information support, which the AACR may tailor to the user speci cs through reinforcing user associations over WordNet associations, leveraging WordNet but not too strongly bound to its detailed structure, which after all was acquired through exposure to large collections of text, not through colloquial usage. Going beyond WordNet, statistical rules of syntax enable the same words to take on different roles, contributing differentially to content. Consider the differences in the way that back contributes to meaning in phrases like (a) Back up, please, and (b) Don t get your back up, please. Phrase (a) is a