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Cross-modal retrieval, which aims to solve the problem that the query and the retrieved results are from different modality, becomes more and more essential with the development of the Internet. In this paper, we mainly focus on the exploration of high-level semantic representation of image and text for cross-modal matching. Deep convolutional image features and Fisher Vector with neural word embeddings...
With the popularity of mobile devices and the quick growth of the mobile Web, users can now browse news wherever they want; so, their news preferences are usually related to their geographical contexts. Consequently, many research efforts have been put on location-aware news recommendation, which recommends to users news happening nearest to them. Nevertheless, in a real-world context, users’ news...
Quite a number of recent works have concentrated on the task of recommending to Twitter users whom they should follow, among which, the WTF (Who To Follow) service provided by Twitter. Recommenders are based either on the user's network structure, or on some notion of topical similarity with other users, or on both. We present a method for analysis of Twitter users supported by a hierarchical representation...
Enormous efforts of human volunteers have made Wikipedia become a treasure of textual knowledge. Relation extraction that aims at extracting structured knowledge in the unstructured texts in Wikipedia is an appealing but quite challenging problem because it's hard for machines to understand plain texts. Existing methods are not effective enough because they understand relation types in textual level...
Machine-learning state-of-the-art keyphrase extraction systems do not take into consideration the fact that part of these keyphrases may not be found in the text. Therefore these systems typically use a training set restricted to textual terms, reducing the learning capabilities of any inductive algorithm. Our research investigates ways to improve the accuracy of these systems by allowing classification...
Named Entity Disambiguation (NED) aims at dis-ambiguating named entity mentions in a text to their corre-sponding entries in a knowledge base such as Wikipedia. Itis a fundamental task in Natural Language Processing (NLP)and has many applications such as information extraction, information retrieval, and knowledge acquisition. In the pastdecade, a number of methods have been proposed for theNED task...
In view of word sense disambiguation shortcomings of the previous methods, they generally do not consider on word distance for computing semantic correlation of the influence of context, as well as the context is limited for ambiguous word sense disambiguation, and the use of part ambiguous context words make word senses more ambiguous. Therefore, this paper proposes the use of dependency parse tree...
A well-known drawback in building machine learning semantic relation detectors for natural language is the lack of a large number of qualified training instances for the target relations in multiple languages. Even when good results are achieved, the datasets used by the state-of-the-art approaches are rarely published. In order to address these problems, this work presents an automatic approach to...
Most traditional Wikipedia based methods use only article content information. By organizing Wikipedia articles as a graph, multi-information such as category and structure information can be utilized in our method. In this paper, we propose a novel method to do classification by using knowledge from a conceptual graph which is built from Wikipedia. First, we build a conceptual graph from Wikipedia...
In e-commerce websites, customers usually make comments, which include the properties of the product, the attitude to the vendor, express delivery information after buying the products. The information provides an important reference when others buy products in the website. In sentiment analysis, a finer-grained opinion mining approach focuses on not only the product itself as a whole but also product...
The proposed framework automatically predicts user tags for online videos from their visual features and associated textual metadata, which is semantically expanded using complementary textual resources.
Data analysis algorithms focused on processing textual data rely on the extraction of relevant features from text and the appropriate association to their formal semantics. In this paper, a method to assist this task, annotating extracted textual features with concepts from a background ontology, is presented. The method is automatic and unsupervised and it has been designed in a generic way, so it...
Knowledge-based data mining and classification algorithms require of systems that are able to extract textual attributes contained in raw text documents, and map them to structured knowledge sources (e.g. ontologies) so that they can be semantically analyzed. The system presented in this paper performs this tasks in an automatic way, relying on a predefined ontology which states the concepts in this...
Most of the traditional text classification methods employ Bag of Words (BOW) approaches relying on the words frequencies existing within the training corpus and the testing documents. Recently, studies have examined using external knowledge to enrich the text representation of documents. Some have focused on using WordNet which suffers from different limitations including the available number of...
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