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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...
Traditional Chinese Medicine (TCM) has been around for over 2000 years and it's a significant part of Chinese cultural heritage. The theoretical framework of TCM is unique and with rich of content, which contains the complex relationships between disease and medicine and has formed a unique system to diagnose and cure illness. Research on question-answering (QA) over TCM is significant for Chinese...
Large scale collaboration is a fundamental characteristic of human society, and has recently manifested in the development and proliferation of online communities. These virtual social spaces provide an opportunity to explore large scale collaborations as natural experiments in which determinants of success can be tested. In order to do this, we first review previous work on meddling online communities...
While a large number of well-known knowledge bases (KBs) in life science have been published as Linked Open Data, there are few KBs in Chinese. However, KBs of life science in Chinese are necessary when we want to automatically process and analyze electronic medical records (EMRs) in Chinese. Of all, the symptom KB in Chinese is the most seriously in need, since symptoms are the starting point of...
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...
Mappings verification is a laborious task. The paper presents a Game with a Purpose based system for verification of automatically generated mappings. General description of idea standing behind the games with the purpose is given. Description of TGame system, a 2D platform mobile game with verification process included in the gameplay, is provided. Additional mechanisms for anti-cheating, increasing...
In contemporary world, translation becomes a critical need of the time. Parallel dictionaries have now become a most accessible source by humans, but confines are there as they do not offer good quality translation function, because of neologisms and words that are out of vocabulary. To overcome this problem in the usage of statistical translation systems is becoming more and more important in maintaining...
Automatic classification of news articles is a relevant problem due to the large amount of news generated every day, so it is crucial that these news are classified to allow for users to access to information of interest quickly and effectively. On the one hand, traditional classification systems represent documents as bag-of-words (BoW), which are oblivious to two problems of language: synonymy and...
Relation discovery is a crucial task in ontology learning process. The classical approaches for relation extraction, based on statistical, syntactical or pattern matching techniques, focus typically on the taxonomic aspect. The discovery of non-taxonomic relationships is often neglected. We extend these approaches by taking into account the document structure which bears additional knowledge. This...
In modern data centers a large amount of energy can be saved by intelligently distributing load on the available servers and transferring idle nodes into low energy modes. Distributing load leads to a more energy-efficient usage of the servers within a server farm. Additionally, the use of energy saving modes like suspend to main memory can decrease the energy consumption dramatically. The selection...
This paper presents a new selection-based question answering dataset, SelQA. The dataset consists of questions generated through crowdsourcing and sentence length answers that are drawn from the ten most prevalent topics in the English Wikipedia. We introduce a corpus annotation scheme that enhances the generation of large, diverse, and challenging datasets by explicitly aiming to reduce word co-occurrences...
Recommender systems have been widely used in our daily life to recommend objects to users meeting the users' preference. In this paper, we focus on objects with temporally variable features such as restaurant with seasonal dishes and point-of-interests (POIs) to have seasonal attractions, and propose a method to automatically generate temporal feature vectors for those objects. The basic idea of the...
In this work, we describe the design, development, and deployment of NEREA (Named Entity Recognizer for spEcific Areas), an automatic Named Entity Recognizer and Disambiguation system, developed in collaboration with professional documentalists. The aim of NEREA is to keep accurate and current information about the entities mentioned in a local repository, and then support building appropriate infoboxes,...
Question answering (QA) is the task of automatically answering a question posed in natural language. Its applied to several domains, and it is a specific type of information retrieval, that has three components such as question processing, information retrieval, and answer extraction. By analysing the user question, we intend to improve the precision of Question answering systems by focusing namely...
Neural language models, such as word embedding, can effectively embed words into vector spaces and preserve linguistic regularities and semantic relationships. However, few researchers have shown their effectiveness on medical terms and relationships. In this paper, we study the applicability of word2vec, a well-known technique for word embedding, to embed medical terms and relations based on different...
Many real-world graphs, including those storing various forms of biological data, are of such large size that storing and processing their information has too high a cost. As a result, one possible solution is to compress the graphs by merging nodes into supernodes. This study introduces a genetic algorithm for graph compression that is based on the similarity of nodes, where two nodes are considered...
In the teaching and learning process, one of the most widely used assessments under the constructivist model is to work in groups. This has many advantages, but also weaknesses such as lack of collaboration, poor organization, difficulty in assessing, poor communication, among others. This paper describes the functionality of the Collaborative Logical Framework and the redesign made by the TEC Digital...
Named Entity Recognition (NER) plays a significant role in Information Extraction (IE). In English, the NER systems have achieved excellent performance, but for the Indonesian language, the systems still need a lot of improvement. To create a reliable NER system using machine learning approach, a massive dataset to train the classifier is a must. Several studies have proposed methods in automatically...
Search engine query logs contain quantities of Named Entities. As the basic work of information extraction, traditional Named-entity extraction methods only can extract specific categories of entities. It is very difficult for them to be applied to the query log Named-entity recognition directly for their limitation. In this paper, a novel approach is proposed to extract Named Entities from user query...
This paper propose a method for automatically linking spatial information that embedded in cultural heritage metadata to geo linked open data. The spatial information that embedded in a cultural heritage object could be used to enrich information of the other objects. For example, by knowing the spatial relation between two or more CH objects, we could conclude the condition of civilization in the...
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