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This paper proposes a systematic full text search on document using a combined keyword and structural similarity of documents under consideration. The approach operates in two steps. The first step uses a set of designated keywords to acquire potential desired documents by means of an open source tool. The second step
We present an index structure to support the approximate keyword search in text databases. In an approximate keyword search query, the user presents a query word Q and a tolerance value k (kges0), and wishes to find all documents in the database that contain the query word Q or any other word in the vocabulary that
semantic net which can be applied to build personalized search engine and tested with single query keyword and multi ones by three different calculating policies. The test results show that it can affect the sort of pages. The personalized search based on vocabulary semantic net improves the quality of search results greatly.
The Web forum is a key tool in new knowledge building among students in learning management systems. Unfortunately, the huge number of messages makes difficult, for tutors and teachers, to quickly evaluate the progress of their students so, an automated support to the analysis is needed. Our solution relies on simple statistical indices inspired by the work in the text analysis field. The obtained...
in both Thai and English is built for helping users from a lot of keywords of the same term and (3) a set of keywords from herbal usages can be combined with the name keyword. From the results, information collected from KUIHerb is useful for searching.
containing a candidate sentence is computed as the cosine of the angle between the question keywords vector and the document vector. Since the semantic feature is more reliable on content verbs and syntactic similarity is suitable for questions with a subject- verb-object syntactic structure, we only consider questions with a
by combining vectors of the named entities and keywords which can express the center vector of the topic more accurately. Then it deals with topic drift by single-pass clustering and continual modification of the topic center. The result of experiments shows that the new method can reduce the rate of missing and false
Web has grown to a huge mass of information resource and is diverse in content. To search such rich source of information one has to be very precise in using keywords in queries to retrieve the relevant documents. Most of the queries issued to search engines are short and have ambiguous context. One way to produce
keywords of different languages are also revealed. We conducted experiments on a set of Chinese-English bilingual parallel corpora to discover the relationships between documents of these languages.
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