This paper proposes two-step generation of a variablelength class-based language model that integrates local and global constraints. In the rst-step, an initial class set is recursively designed using local constraints. Word elements for each class are determined using Kullback divergence and total entropy. In the second step, the word classes are recursively and words are iteratively recreated...

Research rarely reaches the discussion of loan sharks. This study describes how intentions are influenced by service, word-of-mouth, and subjective norms, with knowledge usury acting as a moderating variable. quantitative involved 150 respondents. The data collected was analyzed using structural Equation Modeling (SEM) SmartPLS 3 Software. According to this study, service significantly affected...

We present the first polynomial time algorithm to learn nontrivial classes of languages of infinite trees. Specifically, our algorithm uses membership and equivalence queries to learn classes of ω-tree languages derived from weak regular ω-word languages in polynomial time. The method is a general polynomial time reduction of learning a class of derived ω-tree languages to learning the underlyi...

Selectional preference learning methods have usually focused on word-to-class relations, e.g., a verb selects as its subject a given nominal class. This paper extends previous statistical models to class-to-class preferences, and presents a model that learns selectional preferences for classes of verbs, together with an algorithm to integrate the learned preferences in WordNet. The theoretical ...

OF THE DISSERTATION Context, Word-class, and Prosody in the Recognition of Openand Closed-class Words

The study of word classes has a history of over 4000 years, and the word class problem in over 1000 analytic languages like Modern Chinese can be seen as the Goldbach Conjecture in linguistics. This paper first outlines the existing problems in the POS tagging of Modern Chinese corpora with a case study of 自信. Then it introduces the Two-level Word Class Categorization Model in analytic language...

Three experiments were conducted, involving a total of 180 2 1/2-5-year-old children. The experiments assessed the claim that preschoolers override form class cues in the interest of honoring word-meaning assumptions when they acquire new labels. Children were asked to choose an unlabeled- or a labeled-category object as the referent of a novel word modeled syntactically as a count noun or an a...

An automatic word-classification system has been designed that uses word unigram and bigram frequency statistics to implement a binary top-down form of word clustering and employs an average class mutual information metric. Words are represented as structural tags--n-bit numbers the most significant bit-patterns of which incorporate class information. The classification system has revealed some...

While most traditional word embedding methods target generic tasks, two task-specific dependency-based are proposed for better performance in text classification tasks this work. First, we exploit the dependency parsing tree structure to capture structural information of a sentence, and develop method called (DWE). It finds keywords neighbor words as contexts via parsing. Next, leverage word-cl...