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Due to the huge number of research articles in the biomedical domain, it becomes more and more important to develop methods to find relevant articles of our specific research interests. Keyword extraction is a useful method to find important topics from documents and summarize their major information. Unfortunately
Traditional information gathering systems are mostly keyword-based that are lack of semantic comprehension and analysis ability and can't guarantee the comprehensiveness and accuracy of information gathering. This paper proposes Chinese patent information gathering model based on domain ontology, which can visualize
, such as remove noise words and stop words for word analysis in next stage. Next, we dig entity words, mark the noun which may be the topic entity. And then, we mine key word, make sure the verb or adjective which may be the topic's keyword. Besides, we mine the popular topic, according to the entity words and keywords to
classification. It is grounded in a refined keyword-spotting method that employs: a WordNet-based word lexicon, a lexicon of emoticons, common abbreviations and colloquialisms, and a set of heuristic rules. The approach is implemented through the Synesketch software system. Synesketch is published as a free, open source software
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.