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Fuzzy methodology for enhancement of context understanding.

機譯：模糊方法可增強對上下文的理解。

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摘要

In recent years, research in the domain of Natural Language Understanding (NLU) has benefited from the methodological compilation of context corpora with rich syntax such as the Brown corpora of the Penn Treebank, and machine-readable semantic lexicons such as WordNet. It has also benefited from developments of parsers for syntactic information presentation and semantic meaning retrieval using such corpora. Both types of activities have also advanced the progress in the comprehension of semantic meanings of natural contexts.; The comprehension of semantic meanings is at the heart of the literature of modern linguistics. Inspired largely by the work of the current view of natural language processing, the focus of language processing is isolated linguistic items, research in statistical literature linguistics has focused primarily on the statistical representation of linguistic occurrences. With the help of a wide range of context corpora, this statistical representation ensures a virtually comprehensive coverage of lexical combinational occurrences, which allows a computer program to identify the recurrence of a combination and to further predict the meaning of the recurrence. Due to the limitations of current computer technology, however, representing a lexical combination is restricted to a finite length. Since the statistical representation is confined to describing a finite term-span context, which impedes a computer program from the understanding of contextual meaning, we focus attention on obtaining an approximate but simpler and more satisfactory solutions through soft computing techniques; in particular, fuzzy set theory.; The proposed fuzzy methodology is based on the definition of fuzzy formalism. This involves the comprehensive study of the interplay between the syntactic structure of a verb and the semantic roles that the verb plays. The theory underlying fuzzy formalism is that the syntactic representations associated with a particular verb indicates an explicit range of semantic meanings. Through applying fuzzy formalism to the training corpora, the quality of quantitative representation of linguistic items is improved. (Abstract shortened by UMI.)
機譯：近年來，自然語言理解（NLU）領(lǐng)域的研究受益于具有豐富語法的上下文語料庫（例如Penn Treebank的Brown語料庫）和機器可讀語義詞典（例如WordNet）的方法學(xué)編譯。它也得益于用于此類信息集的語法信息表示和語義含義檢索解析器的開發(fā)。兩種類型的活動也促進了對自然環(huán)境語義含義的理解。語義的理解是現(xiàn)代語言學(xué)文獻的核心。在很大程度上受到當(dāng)前自然語言處理觀點的啟發(fā)，語言處理的重點是孤立的語言項目，統(tǒng)計文獻語言學(xué)的研究主要集中在語言出現(xiàn)的統(tǒng)計表示上。在廣泛的上下文語料庫的幫助下，這種統(tǒng)計表示確保了詞法組合出現(xiàn)的幾乎全面的覆蓋，這使計算機程序可以識別組合的重復(fù)并進一步預(yù)測重復(fù)的含義。然而，由于當(dāng)前計算機技術(shù)的局限性，表示詞法組合被限制為有限的長度。由于統(tǒng)計表示僅限于描述有限的術(shù)語跨度上下文，這妨礙了計算機程序?qū)ι舷挛暮x的理解，因此我們將注意力集中在通過軟計算技術(shù)獲得近似但更簡單且更令人滿意的解決方案上。特別是模糊集理論。所提出的模糊方法論基于模糊形式主義的定義。這涉及對動詞的句法結(jié)構(gòu)與該動詞所扮演的語義角色之間的相互作用的全面研究。模糊形式主義的基礎(chǔ)理論是，與特定動詞相關(guān)的句法表示法表示語義范圍的明確范圍。通過將模糊形式主義應(yīng)用于訓(xùn)練語料庫，提高了語言項目定量表示的質(zhì)量。 （摘要由UMI縮短。）

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  • 作者

    Sun, Yu.;

  • 作者單位

    University of Waterloo (Canada).;

  • 授予單位 University of Waterloo (Canada).;
  • 學(xué)科 Engineering System Science.
  • 學(xué)位 Ph.D.
  • 年度 2005
  • 頁碼 179 p.
  • 總頁數(shù) 179
  • 原文格式 PDF
  • 正文語種 eng
  • 中圖分類 系統(tǒng)科學(xué);
  • 關(guān)鍵詞

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