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Keyword auctions are being used to sell the positions along the side of organic results shown by search engine when user types a keyword or a query related to keyword in a search engine. It has been a huge revenue generating arena for search engines since last decade. Irrespective of the great success of these types
Most educational resource grids are required to support multi-attribute multi-keyword fuzzy-matching queries. But such queries are not efficiently supported in current structured P2P systems. Towards an efficient P2P system capable of processing multi-attribute multi-keyword fuzzy-matching queries with high recall
Precision queries of keyword search developed quickly over relational databases, but it can't be better to process fuzzy queries for satisfying higher requests of users. Aiming at fuzzy queries of numerical attributes for keyword-based search over relational databases, we give a new kind of membership function (normal
Keyword spotting becomes a very important branch of speech recognition. But the acoustic mismatch between training and testing environments often causes a severe degradation in the recognition performance. This paper presents an improved keyword spotting strategy. A fuzzy search algorithm is proposed to extract
KSORD (keyword search over relational database) techniques allow users to obtain information from databases, which is just like using search engines. However, the advanced techniques only realize exact queries, but not for fuzzy queries. The Rocchio algorithm of learning classification is introduced which is made a
schemes allow a user to securely search over encrypted data through keywords and selectively retrieve files of interest, these techniques support only exact keyword search. That is, there is no tolerance of minor typos and format inconsistencies which, on the other hand, are typical user searching behavior and happen very
Traditional keyword-based document clustering techniques have limitations due to simple treatment of words and hard separation of clusters. In this paper, we introduce named entities as objectives into fuzzy document clustering, which are the key elements defining document semantics and in many cases are of user
of vocabulary words in the users speech utterance. In this paper, we investigate an approach that can be deployed in keyword spotting systems. We propose a phoneme classifier that will be ultimately used to provide confidence values to be compared against existing Automatic Speech Recognizer word confidences. The end
This paper proposed a new method of real-time information monitoring and filtering for mobile short messaging service (SMS) system. This method implements the mobile SMS real-time monitoring and filtering by combining the Pinyin fuzzed keyword matching technology with dynamical adjustment of the userspsila credit
Image retrieval is one of the hottest fields of computer vision and pattern recognition. In recent years, many researchers addressed the challenging problem of interpreting the semantics of images. This paper presented a novel approach based on relation net (concept and semantic keyword relation net) for high level
TASTIER is a research project on the new information-access paradigm called type-ahead search, in which systems find answers to a keyword query on-the-fly as users type in the query. In this paper we study how to support fuzzy type-ahead search in TASTIER. Supporting fuzzy search is important when users have limited
multi-keyword business and keyword fuzzy match strategies are used to SMS mobile search. The implementation shows that Business-based SMS mobile search has good efficiency.
based on keyword indexing, there are many records in their result lists that are irrelevant to the user's information needs. It is shown that for retrieving more relevant and precise results, the following two points should be concerned: First of all, the query (either it is generated by a human or an intelligent agent
The content of a text is mainly defined by keywords and named entities occurring in it. In particular for news articles, named entities are usually important to define their semantics. However, named entities have ontological features, namely, their aliases, types, and identifiers, which are hidden from their textual
quotation), payment system, guest's propaganda participation, keyword search-tendering and concentration degree in order form,so this article proposes the fuzzy rejection analysis method,which study the different manufacturer, the different transportation , the different marketing channel for multi-objective decision making
quotation), payment system, guest's propaganda participation, keyword search-tendering and concentration degree in order form,so this article proposes the fuzzy rejection analysis method,which study the different manufacturer, the different transportation , the different marketing channel for multi-objective decision making
answers, such as short-answer questions, discussion questions etc. There are two factors that will affect the subjective item scoring: knowledge point and the nearness level. The unidirectional nearness algorithm in the fuzzy mathematics only focus on the keyword matching, but ignore the scoring of the knowledge point and
matching contexts-dependent keywords and concepts. In the CFS model, a word exact meaning may be determined by other words in contexts. Due to the fact that numerous combinations of words may appear in queries and documents, it may be difficult to define the relations between concepts in all possible combinations. To solve
Most of current spam email detection systems use keywords in a blacklist to detect spam emails. However these keywords can be written as misspellings, for example "baank", "ba-nk" and "bankk" instead of "bank". Moreover, misspellings are changed from time to time and hence spam email detection system needs to
domain, we create a type-2 fuzzy set by assigning relevant weights to the various factors that affect the expertise of the reviewer in that domain. We also create a fuzzy set of the proposal by selecting three keywords that best represent the proposal. We then use a fuzzy functions based equality operator to compute the
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