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This paper studies cross-lingual semantic similarity (CLSS) between five European languages (i.e. English, French, German, Spanish and Italian) via unsupervised word embeddings from a cross-lingual lexicon. The vocabulary in each language is projected onto a separate high-dimensional vector space, and these vector spaces are then compared using several different distance measures (i.e., correlation,...
Nowadays cross-media retrieval is an useful technology that helps people find expected information from the huge amount of multimodal data more efficiently. A common cross-media retrieval framework is first to map features of different modalities into an isomorphic semantic space so that the similarity between heterogeneous data can be measured. For most of semantic space based methods, the mapping...
Semantic analysis is an important component of recommendation systems and information retrieval in computer aided detection. Previous researches have made certain breakthroughs in disease diagnosis and drugs recommended by semantic analysis. We propose a bilateral shortest paths method for computing semantic relatedness based on the human thought patterns for making sufficient use of the hyperlink...
This paper investigates the problem of modeling Internet images and associated text for cross-modal retrieval tasks such as text-to-image search, and image-to-text search. Canonical correlation analysis (CCA), a classic two view approach for mapping text and image into a common latent space, does not make use of the semantic information of text and image pairs. We use CCA to map text, image and semantic...
Now, cross-modal retrieval similarity on multimedia with texts and images have attracted scholars' more and more attention. The difficulty of cross-modal retrieval is how to effectively construct correlation between multi-modal heterogeneous data. According to canonical correlation analysis, most existing cross-modal methods embed the heterogeneous data into a joint abstraction space by linear projections...
We propose a new cross-modal correlation learning framework which boosts the performance of correlation learning models using the hyperlink information. First, we design a neighborhood selection paradigm using the hyperlink structure and content similarities to identify a set of semantically related documents for each multi-modal document in both training and testing stage. Based on the neighborhood...
In this paper, we propose a cross-media regularization framework to enhance image understanding which can benefit image retrieval, classification and so on. The goal of cross-media regularization is to find regularization projections by exploiting the correlations between visual features and textual features. Thus, the original noisy distribution of visual features can be refined by leveraging the...
Nowadays, Internet has been one of the major advertising channels and behavioral targeting has become increasingly important for improving the click-through rate of online advertisements. One of the key research problems in behavioral targeting is how to group users into segments with similar interests or backgrounds. In this paper, we propose a web page-oriented and keywords-based approach to address...
As a free online encyclopedia with a large-scale of knowledge coverage, rich semantic information and quick update speed, Wikipedia brings new ideas to measure semantic correlation. In this paper, we present a new method for measuring the semantic correlation between words by mining rich semantic information that exists in Wikipedia. Unlike the previous methods that calculate semantic relatedness...
Semantic relatedness measurement between words is always a hot issue interested by many researchers. It can be applied to various tasks of NLP and IR with a big challenge. We propose a method for measuring semantic relatedness between words using lexical context in this paper. The method can mainly be divided into two steps. Firstly, for each word of a word-pair, a lexical context is generated exploiting...
Recent research shows the potential of utilizing data collected through Web 2.0 applications to capture domain evolution. Relying on external data sources, however, often introduces delays due to the time spent retrieving data from these sources. The method introduced in this paper streamlines the data acquisition process by applying optimal stopping theory. An extensive evaluation demonstrates how...
Community Question Answering (CQA) has become a popular and effective mean for seeking information on the Web. It is now possible and effective to post a question asked in natural language on a popular community Question Answering (QA) portal, and to rely on other users to provide answers. These online collaborative services are attracting users and questions at an explosive rate, while how to correctly...
With the fast growing development of the Web, the adoption of ontologies to improve the exploitation of information resources, is already heralded as a promising model of representation. However, the relevance of information that they contain requires regular updating, and specifically, the addition of new knowledge. Recently, new research approaches were defined in order to automatically enrich ontology...
A network of concepts is built from Wikipedia documents using a random walk approach to compute distances between documents. Three algorithms for distance computation are considered: hitting/commute time, personalized page rank, and truncated visiting probability. In parallel, four types of weighted links in the document network are considered: actual hyperlinks, lexical similarity, common category...
The amount of multimedia data on personal devices and the Web is increasing daily. Image browsing and retrieval systems in a low-dimensional space have been widely studied to manage and view large numbers of images. It is essential for such systems to exploit an efficient similarity measure of the images when searching for them. Existing methods use the distance in a low-level image feature space...
at the present time, the increase of e-mail spam are heavy to cumber and the spam are vastly spread. These spams cause various problems to the Internet users, such as full incoming mailbox, and wasting time. Therefore, tremendous methods have been proposed but most of them have limitation in the mapping feature and processing time. This paper proposed a method that can detect a set of image e-mail...
An important aspect of trust in cloud computing consists in preventing the cloud provider from misusing the user's data. In this work-in-progress paper, we propose the approach of data anonymization to solve this problem. As this directly leads to problems of cloud usage accounting, we also propose a solution for anonymous yet reliable access control and accountability based on ring and group signatures.
Gossip-based Peer-to-Peer protocols proved to be very efficient for supporting dynamic and complex information exchange among distributed peers. They are useful for building and maintaining the network topology itself as well as to support a pervasive diffusion of the information injected into the network. This is very useful in a world where there is a growing need to access and be aware of many...
The rapid growth in the development of Internet-based information systems increases the demand for natural language interfaces that are easy to set up and maintain. Unfortunately, the problem of deep understanding natural language queries is far from being solved. In this paper, an automated question answering system based on domain ontology and question template is proposed, which does not need deep...
Subjective question is capable of examining the adopting ability of knowledge of the student, but the assessment for it suffers from a number of questions such as trickiness, synonymy and polysemy. This reduces the advantage of subjective question for online exercise. In this paper we explore an approach to automated assessment system for subjective question based on latent semantic indexing. Chinese...
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