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Question classification is an important part of Chinese question answering system, and the result of question classification directly affects the quality of question answering. This paper presents a new method on feature extraction for question classification. HowNet and dependency parsing are used in this new method. The classification experimental results using SVM classifier have shown that the...
Knowledge discovery from the Web is a cyclic process. In this paper we focus on the important part of transforming unstructured information from Web pages into structured relations. Relation extraction systems capture information from natural language text on Web pages, called Web text. However, extraction is quite costly and time consuming. Worse, many Web pages may not contain a textual representation...
This article proposes such a question classification approach that integrates multiple semantic features. It is aimed at these two questions in Chinese question classification models: inaccurate semantic information extraction and too slow processing speed caused by too high Eigenvector dimension. With the help of HowNet and the support vector machine and syntactic and semantic information of question...
With rapid growing popularity, microblogs have become a great source of consumer opinions. Confronting unique properties and massive volume of posts on microblogs, this paper proposes a summarization framework that provides compact numeric summarization for microblogs opinions. The proposed framework is designed to cope with four major tasks: 1) topics detection, 2) sentiment classification, 3) credibility...
Focusing on Chinese subject-predicate constructions, this paper analyzes the limitations of Selectional-Preference based metaphor recognition and proposes a new metaphor recognition model which is based on Semantic Relation Patterns. The model constructs Semantic Relation Pattern by integrating six types of semantic relations between a subject head and other subject heads in a subject-predicate cluster...
This paper investigates how to integrate multi-modal features for story boundary detection in broadcast news. The detection problem is formulated as a classification task, i.e., classifying each candidate into boundary/non-boundary based on a set of features. We use a diverse collection of features from text, audio and video modalities: lexical features capturing the semantic shifts of news topics...
This paper compares two representations of text within the same experimental setting for sentiment orientation analysis, and in particular focuses on the sensitivity of the analysis to sentence length. The two representations compared in this paper are bag-of-words (BoW) and nine dimensional vector (9Dim). The former represents text with a high dimensional feature vector, which ignores grammatical...
As multimedia data come from a wide variety of domains, each having its distinctive data distributions, cross-domain video semantic concept classification becomes an important task in semantic computing. Its challenge arises from the different distribution (in feature space) of the concept between the source and the target domain, which makes a classifier trained on a source domain perform poorly...
For the question of how to classification of cultural relic videos, this paper put forward the analysis method based on semantic and it's two-steps in classification of video: Firstly, based on algorithm that has been used, separating the cultural relic shots and extracting the key frames; Then extracting the features of the key frames. Next training classification of the key frames' features using...
Most of the previous researches on sentiment analysis concentrate on the binary distinction of positive vs. negative. This paper presents the multi-class sentiment classification problem that attempt to mine the implied rating information from reviews. We use four machine learning methods and two feature selection methods to find out whether or not the multi-class sentiment classification problem...
Online reviews are one of the important information resources for people. This paper focuses on a specific domain-movie review and presents a new model for predicting semantic orientation of reviews, i.e., classifying positive reviews from those negative. Different from traditional algorithms for sentiment classifications, this model integrates grammatical knowledge and takes topic correlations into...
In this paper we propose an approach for Chinese question analysis and answer extraction. A general question analysis process contains keyword extraction and question classification. Question classification plays a crucial role in automatic question answering. To implement the question classification, we have carried out experiments with Support Vector Machines (SVM) using four kinds of features:...
Feature selection for text classification is a well-studied problem and the goals are improving classification effectiveness, computational efficiency, or both. In this paper, we propose a two-stage feature selection algorithm based on a kind of feature selection method and latent semantic indexing. Traditional word-matching based text categorization system uses vector space model to represent the...
Mechanical properties are the attributes of a metal to withstand several loads and tensions. More accurately, ultimate tensile strength (UTS) is the force a material can resist until it breaks. The only way to examine this feature is the use of destructive inspections that render the casting invalid with the subsequent cost increment. In our previous researches we showed that the foundry process can...
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