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In pursuing the development of Yanii, a novel keyword based search system on graph structures, in this paper we present the computational complexity study of the approach, highlighting a comparative study with actual PTIME state-of-the-art solutions. The comparative study focuses on a theoretical analysis of different
images automatically. Cluster IDs are adopted to index the characters. A Dream of Red Mansions, a famous classical Chinese literature work including near one million characters, is used to evaluate the performance of Chinese keyword spotting. Experimental results confirm the effectiveness of knowledge-based clustering and
The keyword-based Google images search engine is now becoming very popular for online image search. Unfortunately, only the text terms that are explicitly or implicitly linked with the images are used for image indexing but the associated text terms may not have exact correspondence with the underlying image semantics
This research is concerned with the table based KNN as the approach to the keyword extraction task. The keyword extraction task is viewed as an instance of word classification, and it is discovered that encoding words into tables improved the word categorization performance. In this research, words are encoded into
Keywords are indexed automatically for large-scale categorization corpora. Indexed keywords of more than 20 documents are selected as seed words, thus overcoming subjectivity of selecting seed words in clustering; at the same time, clustering is limited to particular category corpora and keywords indexed feature
huge irrelevant search hits. In this paper, we propose an improved method for ranking of search results to reduce human efforts on locating interesting hits. The search results are re-ranked using adaptive user interest hierarchies (AUIH), which considers both investigator-defined keywords and user interest learnt from
This paper presents the comparison of the text document space dimension reduction and the text document clustering and also the keyword space dimension reduction and keyword clustering by the latent semantic analysis and by the Hebbian neural network with Oja learning rule. Results of this neural network are compared
Remote Electronic Document (CReED) provided an access control to all documents that will grant different privileges to each user of the system. It also utilized a keyword analyzer and result matcher that will make searching and retrieving of documents faster and easier. CReED used a scanner device and file importing tool to
This paper explores a unique way in which the thinking algorithm adds an extra logical substrate to a Web search query using artificial intelligence. Instead of just going after keyword searching, the algorithm tries to assess the motives of the user behind entering a query. The algorithm tries to find the reasons as
Tagging with free form tags is becoming an increasingly important indexing mechanism. However, free form tags have characteristics that require special treatment when used for searching or recommendation because they show much more variation than controlled keywords. In this paper we present a method that puts this
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