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This paper proposes a method for keyword spotting in offline Chinese handwritten documents using a statistical model. On a text query word, the method measures the similarity between the query word and every candidate word in the document by combining a character classifier and four classifiers characterizing the
Existing methods for Blog keyword extraction usually exploit the context in the specified blog. In this paper, we propose to provide a knowledge context by using small number of nearest neighbor blogs to improve keyword extraction performance. Specifically, knowledge context is build by adding several topic related
A lot of semantic information is lost due to keyword centric approach of information indexing. Web search should be based on `context' of the query and not only on the keywords in query. It is only possible when a context from a query as well as document is sensed and which requires a context based indexing approach
Due to the importance of high-quality customer service, many companies use intelligent helpdesk systems (e.g., case-based systems) to improve customer service quality. However, these systems face two challenges: 1) Case retrieval measures: most case-based systems use traditional keyword-matching-based ranking schemes
Big data are generated from a variety of sources having different representation forms and formats, it raises a research question as how important data relevant to a business context can be captured and analyzed more accurately to represent deep and relevant business insight. There is a number of existing big data analytic methods available in the literature that consider contextual information such...
Keyword based search scheme imposes the problem of representing a lot of web pages in the search engines. Query expansion with relevant words increases the performance of search engines, but finding and using the relevant words is an open problem. In this research we describe a new model for query expansion which
Term ambiguity — the challenge of having multiple potential meanings for a keyword or phrase — can be a major problem for search engines. Contextual information is essential for word sense disambiguation, but search queries are often limited to very few keywords, making the available textual context needed for
MRF related AIA approach; we explore the optimal parameter estimation and model inference systematically to leverage the learning power of traditional generative model. Specifically, we propose new potential function for site modeling based on generative model and build local graphs for each annotation keyword. The
Retrieving Proper Names (PNs) specific to an audio document can be useful for vocabulary selection and OOV recovery in speech recognition, as well as in keyword spotting and audio indexing tasks. We propose methods to infer and retrieve OOV PNs relevant to an audio news document by using probabilistic topic models
important in IoT for the Information Systems Research community as well as the first overview of the keywords that the authors use to describe their work in IoT- related context. Publications from the IoT context, including some of the topic areas in smart environment from the AIS electronic library were analyzed towards their
Traditional Information Retrieval (IR) models are based on bag-of-words paradigm, where relevance scores are computed based on exact matching of keywords. Although these models have already achieved good performance, it has been shown that most of dissatisfaction cases in relevance are due to term mismatch between
the system detects keywords "person", "baby" and "feed", we do not want the system to generate "a person feeding a baby" when the actual screen is a scene where the baby is trying to share the food. In this paper, we explore role relationships between objects/persons and their usage in generating a more meaningful video
proposed by biomedical experts. The concepts employed in the models, instead of keywords, guarantee the high recall. And the contexts of each underlying answer sentence boost the precision of answers to each question. We evaluate both methods on the data collection of TREC Genomics Track 2006. The results indicate our methods
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