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International Multiconference on Computer Science and Information Technology, IMCSIT

作者： Farag Ahmed Andreas Nurnberger Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke University of Magdeburg Magdeburg Germany

We present a word sense disambiguation approach with application in machine translation from Arabic to English. The approach consists of two main steps: First, a natural language processing method that deals with the rich morphology of Arabic language and second, the translation including word sense disambiguation. The main innovative features of this approach are the adaptation of the Naive Bayesian approach with new features to consider the Arabic language properties and the exploitation of a large parallel corpus to find the correct sense based on its cohesion with words in the training corpus. We expect that the resulting system will overcome the problem of the absence of the vowel signs, which is the main reason for the translation ambiguity between Arabic and other languages.

关键词： Bayesian methods

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Sub-word Based End-to-End Speech Recognition for an Under-Resourced Language: Amharic

Sub-word Based End-to-End Speech Recognition for an Under-Re...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Nirayo Hailu Gebreegziabher Andreas Nürnberger Faculty of Computer Science Data and Knowledge Engineering Group Otto-von-Guericke University of Magdeburg Magdeburg Germany

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

In this work, we focused on end-to-end speech recognition for less-resourced language, Amharic. The result can be integrated with other tasks such as spoken content retrieval. We explored three models, which consist of Convolutional Neural Networks, Recurrent Neural Networks, and Connectionist Temporal Classification, towards end-to-end speech recognition on less-resourced language. Further, we studied the possibility of having an end-to-end system with 1-best output keeping the network parameters and computational resource minimal. The paper gives attention to finding a more suitable sub-lexical unit for the Amharic end-to-end speech recognition system which can be used as an audio indexing unit. We present the first result comparing grapheme, phoneme, and syllable-based end-to-end speech recognition systems for our target language. The models are evaluated on approximately 52 hours of Amharic speech corpus containing read-speech, audiobooks, and multi-genre radio programs. On the test set, we report a character error rate (CER) of 19.21% and a syllable error rate (SER) of 39.98% for a syllable-based end-to-end model without lexicons and language model integrated.

关键词： Training Error analysis Computational modeling Speech recognition Computer architecture data models Task analysis

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It’s Time to Consider ‘Time’ when Evaluating Recommender-System Algorithms [Proposal]

arXiv

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arXiv 2017年

作者： Beel, Joeran Trinity College Dublin Department of Computer Science and Statistics Knowledge and Data Engineering Group ADAPT Centre Ireland

In this position paper, we question the current practice of calculating evaluation metrics for recommender systems as single numbers (e.g. precision p=.28 or mean absolute error MAE = 1.21). We argue that single numbers express only average effectiveness over usually a rather long period (e.g. a year or even longer). This practice provides only a vague and static view of the data. We propose that recommender-system researchers should instead calculate metrics for time-series such as weeks or months, and plot the results in e.g. a line chart. This way, results show how algorithms’ effectiveness develops over time, and hence the results allow drawing more meaningful conclusions about how an algorithm will perform in the future. In this paper, we explain our reasoning, provide an example to illustrate our reasoning and present suggestions for what the community should do next. Copyright © 2017, The Authors. All rights reserved.

关键词： Recommender systems

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A study in the expressiveness of semantically different policy modelling schemes

A study in the expressiveness of semantically different poli...

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IFIP/IEEE International Symposium on Integrated Network Management

作者： Christos Tsarouchis Declan O'Sullivan David Lewis Knowledge & Data Engineering Group (KDEG) Department of Computer Science & Statistics Trinity College Dublin Dublin Ireland

ISBN: (纸本)9781424492190

Policy engineering is the process of authoring IT management policies, detecting and resolving policy conflicts and revising existing policies to accommodate changing IT resources, business goals and business processes. Policy authoring is often followed by policy enforcement where the actions specified by subjects are performed on targets (resources). In this paper, we study the use of semantically enhanced techniques, such as ontologies, to model resources and their corresponding actions, coupled with a mechanism that can accommodate frequent organizational change, to model policy subjects. For the modeling of policy subjects, the rule-based Community-based Policy management will be used. This integration falls into the category of combining Description Logics (DL) and Logic Programs (LP). We aim to study this integration primarily from the scope of overall system expressivity, but also from the scope of minimizing the cognitive load perceived by policy authors. Such an evaluation can help determine shortfalls in the design of the software system or of the policy model used. To study the balance in modeling with DL and LP techniques, the encoding of part of the Trinity College Dublin statutes will be performed, which is a sufficiently complex real-world example.

关键词： OWL Load modeling Ontologies Welding Analytical models Security Fires

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A Light-weight Convolutional Neural Network based Speech Recognition for Spoken Content Retrieval Task

A Light-weight Convolutional Neural Network based Speech Rec...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Nirayo Hailu Gebreegziabher Andreas Nürnberger Faculty of Computer Science Data and Knowledge Engineering Group Otto-von-Guericke University of Magdeburg Magdeburg Germany

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Convolutional Neural Network has shown to achieve a state of the art performance in computer vision. They have also progressively become popular in speech recognition and other natural language processing tasks. In this study, we aim at designing a light-weight Convolutional Neural Network architecture for the under-resourced end-to-end speech recognition task. We present a carefully designed 1-dimensional Convolutional deep neural network architecture that could achieve reasonable accuracy to be cascaded with spoken content retrieval systems. We explored the usage of Convolutional Neural Networks with Connectionist Temporal Classification under resource-constrained conditions. The possibility of having an end-to-end system with the best decoding result keeping the network parameters and computational time minimum is also shown. The paper presents the results on the Amharic syllable-based end-to-end speech recognition system implementing the designed model. The architecture is trained and evaluated on ≈70 hours of Amharic read-speech, audiobooks, and multi-genre radio programs. On the development set, we report a character error rate of 12.60% and a syllable error rate of 27.28% without language-models integrated. Likewise, on the test set 18.38% character error rate and 27.71% syllable error rate is reached.

关键词： Training Error analysis Computational modeling Speech recognition Computer architecture Convolutional neural networks Task analysis

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Towards a qualitative-informed model for EPR implementation: considering organizational culture

Towards a qualitative-informed model for EPR implementation:...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： R.K. Bali Data Knowledge Engineering Research Group (DKERG School of Mathematical & Information Sciences Coventry University Coventry UK

Information technology (IT) has made a prolific impact, both in sociological and commercial terms. In the commercial world, the pursuit of new technology and working practices has often been at the expense of equal regard for the correct methods to manage the new technology. Contemporary IT techniques and methods include electronic patient records (EPR) which are normally implemented on a local practice or hospital basis. However EPR implementation has major organisational and cultural implications which will form the main focus of the paper.

关键词： Paramagnetic resonance Organizational aspects Information technology Cultural differences Humans Productivity Mathematical model knowledge engineering Technology management Hospitals

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Information retrieval in folksonomies: Search and ranking

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3rd European Semantic Web Conference, ESWC 2006

作者： Hotho, Andreas Jäschke, Robert Schmilz, Christoph Stumme, Gerd Knowledge and Data Engineering Group Department of Mathematics and Computer Science University of Kassel Wilhelmshöher Allee 73 D-34121 Kassel Germany Research Center L3S Expo Plaza 1 D-30539 Hannover Germany

ISBN: (纸本)3540345442

Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. At the moment, however, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Information retrieval systems

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Sentiment classification via L2-norm deep belief network 11

Sentiment classification via L2-norm deep belief network

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20th ACM Conference on Information and knowledge Management, CIKM'11

作者： Liu, Tao Li, Minghui Zhou, Shusen Du, Xiaoyong Key Laboratory of Data Engineering and Knowledge Engineering MOE 100872 Beijing China School of Information Renmin University of China 100872 Beijing China Microsoft Asian Research and Development Group 100080 Beijing China Shenzhen Graduate School Harbin Institute of Technology 518055 Shenzhen China

ISBN: (纸本)9781450307178

Automatic analysis of sentiments expressed in large scale online reviews is very important for intelligent business applications. Sentiment classification is the most popular task of sentiment analysis, which is more challenging than traditional topic-based text classification. Basic features, such as vocabulary words, are not enough to classify sentiments well. Deep Belief Network (DBN) is introduced to discover more abstract features of sentiments. To capture full information of the features, large-size network can be constructed, but at the same time, large-size network tends to over fit the training data and even noise, which will reduce the generalization ability of the network. In this paper, L2-norm Deep Belief Network (L2DBN) is proposed, which uses L2-norm regularization to optimize the network parameters of DBN. L2DBN is first initialized by an unsupervised layer-wise training algorithm, and then fine-tuned by a supervised procedure. Network parameters are optimized using both classification loss and network complexity. Experimental results show that the proposed L2DBN outperforms the state-of-the-art method and the basic DBN on golden, noisy and heterogeneous datasets. © 2011 ACM.

关键词： Sentiment analysis

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Senzi: A sentiment analysis lexicon for the latinised Arabic (Arabizi) 12

Senzi: A sentiment analysis lexicon for the latinised Arabic...

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12th International Conference on Recent Advances in Natural Language Processing, RANLP 2019

作者： Tobaili, Taha Fernandez, Miriam Alani, Harith Sharafeddine, Sanaa Hajj, Hazem Glavaš, Goran Knowledge Media Institute Open University United Kingdom Department of Computer Science and Mathematics Lebanese American University Lebanon Department of Electrical and Computer Engineering American University of Beirut Lebanon Data and Web Science Group Universität Mannheim Germany

ISBN: (纸本)9789544520557

Arabizi is an informal written form of dialectal Arabic transcribed in Latin alphanumeric characters. It has a proven popularity on chat platforms and social media, yet it suffers from a severe lack of natural language processing (NLP) resources. As such, texts written in Arabizi are often disregarded in sentiment analysis tasks for Arabic. In this paper we describe the creation of a sentiment lexicon for Arabizi that was enriched with word embeddings. The result is a new Arabizi lexicon consisting of 11.3K positive and 13.3K negative words. We evaluated this lexicon by classifying the sentiment of Arabizi tweets achieving an F1-score of 0.72. We provide a detailed error analysis to present the challenges that impact the sentiment analysis of Arabizi. © 2019 Association for Computational Linguistics (ACL). All rights reserved.

关键词： Sentiment analysis

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Emergent semantics in BibSonomy

Emergent semantics in BibSonomy

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36th Jahrestagung der Gesellschaft fur Informatik e.V. (GI): Informatik fur Menschen, INFORMATIK 2006

作者： Hotho, Andreas Jäschke, Robert Schmitz, Christoph Stumme, Gerd Knowledge and Data Engineering Group Department of Mathematics and Computer Science University of Kassel Wilhelmshöher Allee 73 D-34121 Kassel Germany Research Center L3S Expo Plaza 1 D-30539 Hannover Germany

ISBN: (纸本)9783885791881

关键词： Semantics

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