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 word...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.展开更多
文摘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.
