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This paper proposes a strategy of the summary sentence selection for query-focused multi-document summarization through extracting keywords from relevant document set. It calculates the query related feature and the topic related feature for every word in relevant document set, then obtains the importance of the word
Identifying prospective customers is an important aspect of marketing research. In this paper, we provide support for a new type of query, the Reverse Top-k Geo-Social Keyword (RkGSK) query. This query takes into account spatial, textual, and social information, and finds prospective customers for geotagged objects
A linguistic analyzer based on KCTs (keyword classification trees) was trained on sentences from the ATIS (Air Travel Information System) air travel task and incorporated into the system (CHANEL) built at CRIM (Centre de Recherche Informatique de Montreal) for the Nov. 1992 ATIS benchmarks. Word sequences were
With the continued proliferation of location-based services, a growing number of web-accessible data objects are geotagged and have text descriptions. An important query over such web objects is the direction-aware spatial keyword query that aims to retrieve the top-k objects that best match query parameters in terms
This paper presents a new keyword extraction algorithm for Chinese news Web pages using lexical chains and word co-occurrence combined with frequency features, cohesion features, and corelation features. A lexical chain is an external performance consistency by semantically related words of a text, and is the
evaluate the collection of words and phrases to select set of keywords of the text. Next use the normal search engine to search the keywords set. Part of the search result will be used as seed links in focused crawler. Focused crawler's crawling policy is the best-first search policy, and this policy uses the similarity
With the growing of wireless communication and mobile applications, more and more users and contents are positioned by geographic locations. The importance of query objects involving both the location and textual proximity of content is highlighted. Existing studies mainly focus on keyword query for static objects or
Efficient multi-keyword fuzzy search over encrypted data is a desirable technology for data outsourcing in cloud storage. The current solutions can only support part of these goals. In this paper, we propose a novel encrypted data searching scheme that can support multiples keywords fuzzy search with high efficiency
Keyword search provides a uniform way of accessing the vast volume of structured and unstructured data present in many enterprises. Existing research on improving the effectiveness of the keyword search has largely focused on result ranking mechanisms, with little consideration given to user feedback. We are working
Cloud user intends to encrypt data before outsourcing sensitive data to the cloud which prevents data searching utility. Hence the necessity for searching through encrypted data appears. But in practice, it is very common for the user to misspell keywords while typing the words. Thus, fuzzy keyword search on encrypted
Spatial keyword querying has attracted considerable research efforts in the past few years. A prototypical query takes a location and keywords as arguments and returns the k objects that score the highest according to a ranking function. While different scoring functions have been used, how to compare different
The proliferation of geo-textual data gives prominence to spatial keyword search. The basic top-k spatial keyword query, returns k geo-textual objects that rank the highest according to their textual relevance and spatial proximity to query keywords and a query location. We define, study, and provide means of
Scientific documents are unstructured data consisting of natural language and hard for scientists to read and manage. Keywords are very helpful for scientists to search the related documents and know about their contents in a prompt way. In this paper we investigate a kind of data preprocessing technique used in SVM
Keywords of academic papers are jargons shared within a research domain as well as a summary of the contents. However, the increasing popularity of the interdisciplinary research in academia in recent years opened a possibility that the choice of keywords would be no longer confined by the traditional domain
We propose a feature word selection method for classifying recommended shops using Yelp customer reviews. TextRank keywords are extracted from the customer reviews to construct the sorted positive and negative keyword lists based on each keyword's summed TextRank scores. The top-K keywords are then aggregated
Matching search technology based on query keyword has been widely used by traditional search way. It still belongs to pure keyword matching and can not acquire satisfactory search results. The essential reason is that traditional Web search lacks semantic understanding to user's search behaviors. In this study, we
Keyword (Feature) selection enhances and improves many Information Retrieval (IR) tasks such as document categorization, automatic topic discovery, etc. The problem of keyword selection is usually solved using supervised algorithms. In this paper, we propose an unsupervised approach that combines keyword selection and
Text chance discovery is the process of extracting author's potential hidden issue from a large number of texts. For the main question keyword (i.e. Chance) extracting, we propose a framework of text chance discovery system based on immune and multi-agent in this paper. By immunization and agent self-learning, this
This paper proposes a new re-ranking scheme and presents experimental performance results for Web image retrieval with integrated query. In our previous work, cross-modal association rule was designed for associating one keyword with several visual feature clusters in Web image retrieval. Based on the cross-modal
citation recommendation suffers with the following three limitations. First, most of the existing approaches for citation recommendation require input in the form of either the full article or a seed set of citations, or both. Nevertheless, obtaining the recommendation for citations given a set of keywords is extremely useful
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