Earticle

The study on word similarity computation plays an important role in natural language processing (NLP). Recently the algorithm based on HowNet is widely used and proves to work well in Chinese word similarity computation. However, the relationship between the number of brother nodes and the fineness of the hierarchy is not considered. This paper investigates the ratio of two words on the brother nodes’ number called sememe probability density and proposes an improved algorithm based on HowNet. The results indicate that the correlation measure of the algorithm presented by this paper is 75.4%, and it is much better than the major state-of-the-art method (68.1%).

Sentiment polarity analysis on the microblogging hot topic can better understand the Internet user's attitude and tendency toward a specific event, so that government can effectively guide the public opinion. Different from other methods, the sentiment polarity analysis method put forward in this paper gives full consideration to the expression characteristics of microblogging, a particular network media. It particularly considered the network languages and emoticons when calculated the value of sentiment polarity. In this method, we calculate the value of sentiment polarity of each word firstly by combining Pointwise Mutual Information (PMI) and HowNet. Secondly, modify the sentiment polarity value of a word through syntactic dependencies. Finally, accumulate the sentiment polarity value of each word, so the sentiment polarity value of a microblogging can be obtained. The sentiment polarity value of a hot topic can be obtained in the end, through accumulating the sentiment polarity value for all microblogging in a hot topic. Contrast experiment results show that, the method can obtain the sentiment polarity value of a hot topic more accurately and effectively.

본 연구에서는 추상적 사고를 가능하게 해주는 개념은유 표현의 서술어를 분석하여, 추상개념의 근원영역을 찾는 알고리즘을 HowNet 지식 시스템을 이용하여 제안하고자 한다. 실제로 추상개념 '문화' 가 쓰인 242개의 은유 표현 용례 문장을 가지고 제안된 알고리즘으로 근원영역을 찾고. 이를 토대로. 목표영역 '문화' 의 근원영역이 추론기에 의하여 자동적으로 추론되는 HowNet 기반 은유 온톨로지의 구축 방안을 제시하고자 한다. 또한, 한국어 '문화' 와 영어표현 'Culture'의 근원영역 비교를 통하여 구축된 온톨로지를 영어 번역 및 작문에 어떻게 활용할 수 있는지 보이고자 한다.

This paper explains how we define and represent modality in E-HowNet. Following Lyons (1977, reviewed in Hsieh 2003, among others), we hold that modals express a speaker's opinion or attitude toward a proposition and hence have a pragmatic dimension and recognize five kinds of modal categories, i.e. epistemic, deontic, ability, volition and expectation modality. We then present a representational formalism that contains the three most basic components of modal meaning: modal category, positive or negative and strength. Such a formula can define not only modal words but also words that contain modal meanings and cope with co-compositions of modals and the negation construction.