The Review of Automatic Chinese Word Segmentation

As the fundamental work of Chinese information processing, Chinese word segmentation has achieved great progress since its birth. This paper reviews the research status of the CWS, discusses the formal model of automatic word segmentation, and analyzes the difficulties of word segmentation.

Research on Automatic Recognition of Separable Words in Modern Chinese

Separable words have important applications in many fields such as Chinese information processing, Chinese-English translation, teaching Chinese as a foreign language. There are about five thousand separable words distribute in the corpus of Chinese, and the word frequency is greater in the novel, so the study on identification of separable words is significant. This paper selects the higher discrete frequency of verb-object separable words as the object of the study, by examining the manifestation of extended components in different separable words and giving summary and detailed classification of the extended components on the large-scale corpus, a new approach is designed based on the words segmentation and the structure type of extended component. According to the experiments of identification mark to separable words of verb-object type, the average recall is 89.54% and the average precision is 87.43% in open test. The experimental results show that the method is effective.

Overview of Chinese Word Segmentation Method

This article provides a brief introduction to Natural Language Processing and basic knowledge of Chinese Word Segmentation at first. Chinese Word Segmentation is a process of turning a series of Chinese characters into a series of Chinese words with some rules. As the fundamental component of Chinese information processing, it is wildly used in correlative areas. Accordingly, research on Chinese Word Segmentation has important theoretic and realistic meaning. In this paper, we mainly introduces the challenge in Chinese Word Segmentation, and presented the categories of Chinese Word Segmentation method.

Research on Sustainable Mining Engineering

The concepts of Chinese information processing and natural language processing (NLP) and their development tendency are summarized. There are different comprehension of Chinese information processing and natural language processing in China and the other countries. But the work appears to emerge in the study of key point of languages processing. Mining engineering is very important for our country. Though the final task of languages processing is difficult, Chinese information processing has contributed substantially to our scientific research and social economy and it will play an important part for mining engineering in our future.

Learning Hierarchical Lexical Hyponymy

Chinese information processing is a critical step toward cognitive linguistic applications like machine translation. Lexical hyponymy relation, which exists in some Eastern languages like Chinese, is a kind of hyponymy that can be directly inferred from the lexical compositions of concepts, and of great importance in ontology learning. However, a key problem is that the lexical hyponymy is so commonsense that it cannot be discovered by any existing acquisition methods. In this paper, we systematically define lexical hyponymy relationship, its linguistic features and propose a computational approach to semi-automatically learn hierarchical lexical hyponymy relations from a large-scale concept set, instead of analyzing lexical structures of concepts. Our novel approach discovered lexical hyponymy relation by examining statistic features in a Common Suffix Tree. The experimental results show that our approach can correctly discover most lexical hyponymy relations in a given large-scale concept set.