RESEARCH ON IP FLOW FORMAT AND SEMANTIC FORMALIZATION

Malformed packets and overlapping fragments are harmful to Intranet end hosts. A formalization engine was introduced to formalize transit packets and reassemble fragments to eliminate the fragment semantic ambiguity. In the formalization engine, malformed packets are formalized by a packet verification engine layer according to protocol standards. In order to eliminate the fragment semantic ambiguity, OS classes of end hosts were collected by an OS detector, each fragment was reassembled according to its OS class. According to the reassembly algorithm of different OS, the pre-forward fields of the cached data were counted with the application of the pre-forward policies and were transmitted to save system resource. Applying the BSD-Linux pre-forward policy, the BSD-right pre-forward policy and the First pre-forward policy, the packet loss rate is dropped and system performance improved. The experiments show that the identification precision can be maintained about 90% in heavy processing load.

Homonyms Discovery in Folksonomy Based on User Community Analysis

to the capability of reflecting social perception on semantic of resources, folksonomy has been proposed to improve the social learning for education and scholar researching. However, its actual impact is significantly influenced by the semantic ambiguity problem of tags. So, in this paper, we proposed a novel way of detecting homonyms, one of the main sources of tag＇s semantic ambiguity problem, in noisy folksonomies. The study is based on two hypotheses： 1） Users having different interests tend to have different understanding of the same tag. 2） Users having similar interest tend to have common understanding of the same tag. Therefore, we firstly discover user communities according to users＇ interests. Then, tag contexts are discovered in subsets of folksonomy on the basis of user communities. The experimental results show that our method is effective and outperform the method finding tag contexts using all tags in folksonomy with overlapping clustering algorithm especially when various users having different interests are contained by the folksonomyo

An Analysis of the Linguistic Features of The Minister’s Black Veil from the Perspective of Literary Pragmatics

The Minister’s Black Veil is one of the most classic short stories written by American romantic writer Nathaniel Hawthorne(1804-1864),From the perspective of literary pragmatics,this paper analyzes the language features of the novel,such as words and sentences,grammar,semantic ambiguity,rhetoric and conversational implicature based on cooperative principle,so as to explore the superb writing style and literary art of the novel,better understand and appreciate this literary work,and provide a new perspective and reference for the study of British and American literature Direction.

An Analysis of Homonymy and Polysemy

Vocabulary is very important for language learning,and it is an important part of human communication,thus mastering vocabu-lary is essential for language acquisition.To explore the similarities and differences of word meaning,to distinguish the differences be-tween polysemy and homonymy and to enlarge students' vocabulary,are the significant part of class teaching.It mainly discusses thehomonymy and polysemy from the definition,the principles of distinction and semantic ambiguity,further clarifying the differences be-tween the two as well as leaving some implications for the future application of learning.

Study on Chinese Webpage Keyword Extraction based on Multiple Index Factors

Webpage keyword extraction is very important for automatically extracting webpage summary, retrieval, automatic question answering, and character relation extraction, etc. In this paper, the environment vector of words is constructed with lexical chain, words context, word frequency, and webpage attribute weights according to the keywords characteristics. Thus, the multi-factor table of words is constructed, and then the keyword extraction issue is divided into two types according to the multi-factor table of words： keyword and non-keyword. Then, words are classified again with the support vector machine （SVM）, and this method can extract the keywords of unregistered words and eliminate the semantic ambiguities. Experimental results show that this method is with higher precision ratio and recall ratio compared with the simple ff/idf algorithm.