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Named entities such as people, locations, and organizations play a vital role in characterizing online content. They often reflect information of interest and are frequently used in search queries. Although named entities can be detected reliably from textual content, extracting relations among them is more challenging, yet useful in various applications (e.g., news recommending systems). In this...
A context model plays a significant role in developing context-aware architectures and consequently on realizing context-awareness, which is important in today's dynamic computing environments. These architectures monitor and analyse their environments to enable context-aware applications to effortlessly and appropriately respond to users' computing needs. These applications make the use of computing...
Discovering topics in short texts, such as news titles and tweets, has become an important task for many content analysis applications. However, due to the lack of rich context information in short texts, the performance of conventional topic models on short texts is usually unsatisfying. In this paper, we propose a novel topic model for short text corpus using word embeddings. Continuous space word...
FAQs are the lists of common questions and answers on particular topics. Today one can find them in almost all web sites on the internet and they can be a great tool to give information to the users. Questions in FAQs are usually identified by the site administrators on the basis of the questions that are asked by their users. While such questions can respond to required information about a service,...
Synonym-based searching is considered to be a complicated problem, as text mining from unstructured data of web is challenging. Finding useful information which matches user need from the bulk of web pages is a cumbersome task. In this paper, a novel and practical synonym retrieval technique is proposed for addressing this problem. For replacement of semantics, user intent is taken into consideration...
Quantifying the semantic relation between words is a key element in several applications including the treatments at the meaning level. A great variety of approaches are proposed in order to quantify the semantic proximity between concepts or words. These approaches exploit computational models including the hierarchical and textual information of the semantic resources. Among these models, the distributional...
Today, E-Commerce has become the largest revenue generation industry, letting seller sell everything from a pen to plane to the customers across the globe. Over an E-commerce platform where user and vendor merely interact with each other, the trust is undeniably the most important factor for users to perform transactions online. But at the same time it can't be assessed directly using some pre-defined...
User-contributed content on the Internet has been growing at an extraordinary pace. Ranking vast amounts of such content, such as digital photographs, is handled well through user-driven ranking. It helps speeding up the ranking process while reflecting the opinions of the community. However, user-driven ranking can be often subjective and difficult to compare. We solve this using a well-known mathematical...
The increasing growth of cities, the concurrent trend toward "smart" environments put the topic of mobility into the focus of information service developers. The increasing prevalence of smart devices allows an enhanced automated harvesting of data that can be processed to satisfy information needs of the actors within the mobility system thus, providing an environment for frequent interaction...
Internet inquiry is playing an increasingly important role as the complement of the traditional medical service system, especially the similar cases recommendation. It can not only save the patients' waiting time, but also make use of the historical resources, for many cases with the same purpose have been solved perfectly. However, because of the diversity and non-standard of the patients' descriptions,...
There is an emerging international phenomenon of drugs that are sold without any control on online marketplaces. An example of a former online marketplace is Silk Road, best known as a platform for selling illegal drugs operated as a Tor hidden service. Silk Road was closed by FBI in 2013 but new alternatives have appeared since illicit substances is a big market. One problem with online marketplaces...
Entity linking (EL) is the task of mapping name mentions in web text to their entities in a knowledge base. Most of earlier EL work in the knowledge based approach is usually formulated as a ranking problem, either by (i) non-collective approaches with supervised models, or (ii) collective approaches by leveraging global topical coherence which means semantic relations between entities through graph-based...
The number of entities in large-scale knowledge bases has been growing in recent years. The key issue to entity linking using a knowledge base such as Wikipedia is entity disambiguation. The objective of our proposing system is to disambiguate entities in documents and link entity mentions to their corresponding Wikipedia articles. To this end, our system ranks the set of candidate entities based...
Community-based Question Answering (CQA) services are becoming popular as the public gets used to look for help and obtain information. Existing CQA services try to recommend someone for answering new questions. On the other hand, people are allowed to exchange information and experience using various collaborative tools. It would be interesting to combine the two approaches to increase the reliability...
In this study, subjects had been collected from Wikipedia web pages by using links. Subjects had been connected with other subjects. Context of Wikipedia pages had been used for defining power of link between subjects. Ontology graph is created with subjects and power of links. Main subjects of given documents had been calculated with the Ontology graph. For calculation, all subjects had been found...
This paper presents a case study of discovering and classifying verbs in large web-corpora. Many tasks in natural language processing require corpora containing billions of words, and with such volumes of data co-occurrence extraction becomes one of the performance bottlenecks in the Vector Space Models of computational linguistics. We propose a co-occurrence extraction kernel based on ternary trees...
The extraction of semantic contexts is a relevant issue in information retrieval to provide high quality query results. This paper introduces the semantic context underlying a set of given input concepts as defined by the relevant multiple explanation paths connecting the input concepts in a collaborative network. A pheromone-like model based on this approach is introduced for the detection and the...
Today, user generated content and online shared opinions are gaining relevance as a source of information not only for other consumers but also for retailers. However, the huge number of posted opinions makes difficult any manual analysis. This paper proposes a new approach for gender discourse analysis based on the semantic analysis of the content of shared reviews in electronic word of mouth communities...
Existing work in the semantic relatedness literature has already considered various information sources such as WordNet, Wikipedia and Web search engines to identify the semantic relatedness between two words. We will show that existing semantic relatedness measures might not be directly applicable to microblogging content such as tweets due to i) the informality and short length of microblogging...
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...
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