Chinese noun-classifier compound words show different properties in lexical meaning and syntactic collocation. The complexity of these compounds lies in the grammaticalization of classifiers’ system. Some nouns used...Chinese noun-classifier compound words show different properties in lexical meaning and syntactic collocation. The complexity of these compounds lies in the grammaticalization of classifiers’ system. Some nouns used as classifiers in the position of classifier are not grammaticalized into real classifiers which are temporary classifiers. So some of them are real noun-classifier compounds and some belong to noun-noun compounds. This paper attempts to analyze the structural relation between noun morpheme and classifier morpheme within the word. It also explains that the noun and the classifier compound in the position of light noun in the nP structure.展开更多
The proliferation of forums and blogs leads to challenges and opportunities for processing large amounts of information. The information shared on various topics often contains opinionated words which are qualitative ...The proliferation of forums and blogs leads to challenges and opportunities for processing large amounts of information. The information shared on various topics often contains opinionated words which are qualitative in nature. These qualitative words need statistical computations to convert them into useful quantitative data. This data should be processed properly since it expresses opinions. Each of these opinion bearing words differs based on the significant meaning it conveys. To process the linguistic meaning of words into data and to enhance opinion mining analysis, we propose a novel weighting scheme, referred to as inferred word weighting(IWW). IWW is computed based on the significance of the word in the document(SWD) and the significance of the word in the expression(SWE) to enhance their performance. The proposed weighting methods give an analytic view and provide appropriate weights to the words compared to existing methods. In addition to the new weighting methods, another type of checking is done on the performance of text classification by including stop-words. Generally, stop-words are removed in text processing. When this new concept of including stop-words is applied to the proposed and existing weighting methods, two facts are observed:(1) Classification performance is enhanced;(2) The outcome difference between inclusion and exclusion of stop-words is smaller in the proposed methods, and larger in existing methods. The inferences provided by these observations are discussed. Experimental results of the benchmark data sets show the potential enhancement in terms of classification accuracy.展开更多
文摘Chinese noun-classifier compound words show different properties in lexical meaning and syntactic collocation. The complexity of these compounds lies in the grammaticalization of classifiers’ system. Some nouns used as classifiers in the position of classifier are not grammaticalized into real classifiers which are temporary classifiers. So some of them are real noun-classifier compounds and some belong to noun-noun compounds. This paper attempts to analyze the structural relation between noun morpheme and classifier morpheme within the word. It also explains that the noun and the classifier compound in the position of light noun in the nP structure.
文摘The proliferation of forums and blogs leads to challenges and opportunities for processing large amounts of information. The information shared on various topics often contains opinionated words which are qualitative in nature. These qualitative words need statistical computations to convert them into useful quantitative data. This data should be processed properly since it expresses opinions. Each of these opinion bearing words differs based on the significant meaning it conveys. To process the linguistic meaning of words into data and to enhance opinion mining analysis, we propose a novel weighting scheme, referred to as inferred word weighting(IWW). IWW is computed based on the significance of the word in the document(SWD) and the significance of the word in the expression(SWE) to enhance their performance. The proposed weighting methods give an analytic view and provide appropriate weights to the words compared to existing methods. In addition to the new weighting methods, another type of checking is done on the performance of text classification by including stop-words. Generally, stop-words are removed in text processing. When this new concept of including stop-words is applied to the proposed and existing weighting methods, two facts are observed:(1) Classification performance is enhanced;(2) The outcome difference between inclusion and exclusion of stop-words is smaller in the proposed methods, and larger in existing methods. The inferences provided by these observations are discussed. Experimental results of the benchmark data sets show the potential enhancement in terms of classification accuracy.
