27-28, January 2018, Dubai, UAE

Accepted Papers

Accepted Papers

  • A Domain Independent Approach For Ontology Semantic Enrichment
    Tahar Guerram and Nacima Mellal ,Departement of Mathematics and Computer Science,University Larbi Ben M’hidi of Oum El Bouaghi - ALGERIA

    Ontology automatic enrichment consists of adding automatically new concepts and/or new relations to an initial ontology built manually using a basic domain knowledge. In a concrete manner, enrichment is firstly, extracting concepts and relations from textual sources then putting them in their right emplacements in the initial ontology. However, the main issue in that process is how to preserve the coherence of the ontology after this operation. For this purpose, we consider the semantic aspect in the enrichment process by using similarity techniques between terms. Contrarily to other approaches, our approach is domain independent and the enrichment process is based on a semantic analysis. Another advantage of our approach is that it takes into account the two types of relations, taxonomic and non taxonomic ones.

  • Multi-channel online discourse as an indicator for Bitcoin price and volume
    Marvin Aron Kennis 1Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands

    This research aims to identify how Bitcoin-related news publications and online discourse are expressed in Bitcoin exchange movements of price and volume. Being inherently digital, all Bitcoin-related fundamental data (from exchanges, as well as transactional data directly from the blockchain) is available online, something that is not true for traditional businesses or currencies traded on exchanges. This makes Bitcoin an interesting subject for such research, as it enables the mapping of sentiment to fundamental events that might otherwise be inaccessible. Furthermore, Bitcoin discussion largely takes place on online forums and chat channels. In stock trading, the value of sentiment data in trading decisions has been demonstrated numerous times [1] [2] [3], and this research aims to determine whether there is value in such data for Bitcoin trading models. To achieve this, data over the year 2015 has been collected from Bitcointalk.org, (the biggest Bitcoin forum in post volume), established news sources such as Bloomberg and the Wall Street Journal, the complete /r/btc and /r/Bitcoin subreddits, and the bitcoin-otc and bitcoin-dev IRC channels.

    By analyzing this data on sentiment and volume, we find weak to moderate correlations between forum, news,and Reddit sentiment and movements in price and volume from 1 to 5 days after the sentiment was expressed. A Granger causality test confirms the predictive causality of the sentiment on the daily percentage price and volume movements, and at the same time underscores the predictive causality of market movements on sentiment expressions in online communities.

  • Smart Luganda Language Translator
    Fagbolu Olutola1 and Obalalu Babatunde2 1Department of Computer Science, School of Computing and Information Technology Kampala International University, Kampala, Uganda2Department of Computer Science, University of Ibadan, Ibadan, Nigeria

    Luganda is a Bantu language that is prevalent in Uganda and widely spoken by over five million people, It describes how the Source Language (Luganda) is digitally process based on developed corpus which is suitable for example-based text and speech-input machine translation into Target Language (English). Finite-State Transducers were deployed and trained to translate only text from Luganda to its corresponding English Language equivalent. Mobile application versatility help to develop Smart Luganda Translator so as to solve most of the problems associated with translation, improve practices and use of technology by strengthening the existence of Luganda Language. This research work will enhance social status of Luganda people, alleviate attendant flaws of translation and other sundry matters of the speakers of Luganda.

  • Towards Making Sense Of Online Reviews Based On Statement Extraction
    Michael Rist1, Ahmet Aker1 and Norbert Fuhr1, 1Department of Computer Science and Applied Cognitive Science, University

    Product reviews are valuable resource for information seeking and decision making purposes. Products such as smart phone are discussed based on their aspects e.g. battery life, screen quality, etc. Knowing user statements about aspects is relevant as it will guide other users in their buying process. In this paper, we automatically extract user statements about aspects for a given product. Our extraction method is based on dependency parse information of individual reviews. The parse information is used to learn patterns and use them to determine the user statements for a given aspect. Our results show that our methods are able to extract potentially useful statements for given aspects.

  • A New Method Of Teaching Figurative Expressions To Iranian Language Learners
    Leila Erfaniyan Qonsuli1 and Mostafa Bahraman1,1Assistant Professor, Kashmar higher education institute, Iran

    In teaching languages, if we only consider direct relationship between form and meaning in language and leave psycholinguistic aside, this approach is not a successful practice and language learners won't be able to make a successful relation in the real contexts. The present study intends to answer this question: is the teaching method in which salient meaning is taught more successful than the method in which literal or figurative meaning is taught or not? To answer the research question, 30 students were selected. Every ten people are formed as a group and three such groups were formed. Twenty figurative expressions were taught to every group. Group one was taught the figurative meaning of every expression. Group two was taught the literal meaning and group three was taught the salient meaning. Then three groups were tested. After analyzing data, we concluded that there was a significant difference in mean grades between classes and the class trained under graded salience hypothesis was more successful. This shows that traditional teaching methods must be revised.

  • Twitter Sentiment Analysis For Amharic Using Machine Learning Techniques
    Yirga Badma1 and Hyoung Joong Kim1,1Department of Cyber Defense, Korea University, Seoul, Korea

    Sentiment analysis determines the sense (feeling) of humans about a certain topic. In this paper, we used machine learning techniques to classify Amharic tweets into positive, negative or neutral after labelling with a lexicon classifier. In the learning phase, we have used naïve Bayes classifier with unigram features. We also used Support vector machines and logistic regression classifiers over a word-embedding model. Our best result was from SVM with an accuracy of 77.27% outperforming both naïve Bayes and logistic regression methods.

  • Machine Translation and the Problems of Translating Cultural Terms in the Arab World
    Dr. SEKHRI Ouided,Department of Arts and English language,Mentouri Brothers University- Constantine

    Translation is one of the most complex issues in modern Arab culture, thought and development. Despite the fact that there are collective efforts of individuals, organisations and government policies, the results continue to be modest and not as expected. The issues that contribute in the translation crisis in the Arab world are not the only ones because digital technology has changed the rules of the game almost entirely. As a result, translation, in the traditional sense is no longer accessed through papers and books, but via screens, platforms, social networks and all that is online. This is the speedily changing world of machine translation. The aim behind this paper is to examine the status of machine translation in the Arab world in relation to culture specific terms. Specifically, this work analyses the challenges of localising machine translation of cultural terms and the problems that translators face when using these technological devices, proposes some of the limitations in both policy and pedagogy that the field is currently facing. Only a few universities have conducted research in this field. It is recommended that more attention be paid and more research be conducted to get the most use out of this technology and that more efficient Arabic machine translation systems that suit the translation of cultural terms specifically between Arabic and English be developed.

  • Query Processing On Natural Language Interface To Database A knowledge Based Approach
    Raji Sukumar A1 and Babu Anto P1,1Department of Information Technology, Kannur University, Kannur, Kerala India.

    Database is the heavily used persistent storage, which is the major source of information. User interaction with database is possible with Structured Query Language (SQL) by a skilled person, where as an unskilled person there is a demand for intelligent interfaces. This leads to the development of Intelligent Database Systems (IDBMS). And prompted to the development of Natural Language Interface to Database (NLIDB). This paper evaluates intelligent techniques of NLIDB in Malayalam (a south Indian language of Dravidian family. This work mainly concentrates on the knowledge extraction of Natural Language Query, based on the intelligent technique Ontology, and studies the linguistic problems of Malayalam by processing the language with Natural Language Processing (NLP). This work also develops a Malayalam Language Train Time Enquiry System for the Railway stations in Kerala (MLTTES-Kerala), where there involve a time Ontology. The investigator tries to extract the hidden knowledge behind a Natural Language Query by processing through the different levels of. The results are discussed on two-stage retrieval model the precision is calculated, after first and second stage of retrieval the result has been compared in its natural language understanding levels. First 1000 queries have been submitted and evaluate the result obtained in morphological, lexical, syntactic and semantic levels will give 100% result. The same experiment is repeated with another 1000 queries of same corpus and the result is 98% because of the poor background knowledge. The error detection is performed by improving the knowledge the system could achieve 100% result.

  • Informatized Caption Enhancement Based On IBM Watson API And Speaker Pronunciation Time-DB
    Yong-Sik Choi1, YunSik Son1 and Jin-Woo Jung1, 1Department of Computer Science and Engineering, Dongguk University, Seoul, Korea

    This paper aims to improve the inaccuracy problem of the existing informatized caption in the noisy environment by using the additional caption information. The IBM Watson API can automatically generate the informatized caption including the timing information and the speaker ID information from the voice information input. In this IBM Watson API, when there is noise in the voice signal, the recognition results are not good, causing the informatized caption error. Especially, it is more easily found in movies such as background music and special sound. Specifically, to reduce caption error, additional captions and voice information are entered at the same time, and the result of the informatized caption of voice information from IBM Watson API is compared with the original text to automatically detect and modify the error part. Based on the database containing the average pronunciation time, each word for each speaker is changed into the informatized caption in this process. In this way, more precise informatized captions could be generated based on the IBM Watson API.

  • Twitter Sentiment Analysis of New IKEA Store Using Machine Learning
    Yujiao Li and Hasan Fleyeh,School of Technology and Business Studies Dalarna University

    The aim of this paper is to study public opinion concerining opening new IKEA store. It also aims to discover how opinion is evolved and performed in different cities investigated in this study. Twitter texts, which is invoked by public to express opinion concerning this event, is used as suitable data source to implement sentiment analysis. English and Swedish tweets are investigated, but Swedish language processing has been seldom conducted. Therefore, it is necessary to develop Swedish sentiment prediction model to deal with Swedish tweets. For this purpose, this study examined a series of approaches and models to figure out the optimal classification method to classify Swedish tweets. Evaluation of the different approaches confirmed that elastic net outperformed other approaches and has been selected to predict the Swedish tweets. This method was able to discove relevant features for sentiment classification so as to increase the precision.

  • Semi Supervised Graph Based Keyword Extraction Using Lexical Chains And Centrality Measures
    Chhavi Sharma1, Ayush Aggarwal2 and Minni Jain3 1,2Dept of Information Technology, DTU, Delhi, India3Associate Professor, DTU, Delhi, India

    This paper presents keyword extraction using lexical chains and graph centrality measures, derived from the semantic similarity of the words (using WordNet). We present our hypothesis in a small-world approach where we say that while every paragraph in a document is constrained to one local point, the document in all is centered on one global concept. Thus the primary objective is to yield greater precision and recall using this previously unexplored technique.

  • Culture Teaching In English Teaching In Military Academies
    Zhang Yanjuan1, Wang Fang1 and Zhu Qingqin1,1 Department of Foreign Language Teaching and Research, Army Academy of Artillery and Air Defense, Hefei, Anhui Province, China

    With the increasing communication and cooperation between PLA and foreign armies, the army needs lots of foreign language talents who have the intercultural communicative competence. From this aspect, foreign language teaching in military academies is not only to train cadets’ basic language skills such as listening, speaking, reading, writing and translating, but also to make cadets know the cultural background of foreign countries and social customs. However, traditional teaching method is still dominant in military English education, which cannot meet the social need for high-quality army officers. The author proposes the model of incorporating culture teaching into English teaching for cadets and the useful techniques of culture teaching in the teaching practice.