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1st Workshop on Graph-Based Algorithms for Natural Language Processing, Textgraphs 2006 at Human Language Technologies

作者： Hassan, Hany Hassan, Ahmed Noeman, Sara IBM Cairo Technology Development Center P.O. Box 166 Al-Ahram Giza Egypt

Classification techniques deploy supervised labeled instances to train classifiers for various classification problems. However labeled instances are limited, expensive, and time consuming to obtain, due to the need of experienced human annotators. Meanwhile large amount of unlabeled data is usually easy to obtain. Semi-supervised learning addresses the problem of utilizing unlabeled data along with supervised labeled data, to build better classifiers. In this paper we introduce a semi-supervised approach based on mutual reinforcement in graphs to obtain more labeled data to enhance the classifier accuracy. The approach has been used to supplement a maximum entropy model for semi-supervised training of the ACE Relation Detection and Characterization (RDC) task. ACE RDC is considered a hard task in information extraction due to lack of large amounts of training data and inconsistencies in the available data. The proposed approach provides 10% relative improvement over the state of the art supervised baseline system. © 2006 Association for Computational Linguistics

关键词： Information retrieval

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ibm_EG-CORE: Comparing multiple Lexical and NE matching features in measuring Semantic Textual similarity 2

IBM_EG-CORE: Comparing multiple Lexical and NE matching feat...

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2nd Joint Conference on Lexical and Computational Semantics, *SEM 2013

作者： Noeman, Sara IBM Cairo Technology and Development Center P.O. Box 166 Al-Ahram Giza Egypt

ISBN: (纸本)9781937284480

We present in this paper the systems we participated with in the Semantic Textual Similarity task at SEM 2013. The Semantic Textual Similarity Core task (STS) computes the degree of semantic equivalence between two sentences where the participant systems will be compared to the manual scores, which range from 5 (semantic equivalence) to 0 (no relation). We combined multiple text similarity measures of varying complexity. The experiments illustrate the different effect of four feature types including direct lexical matching, idf-weighted lexical matching, modified BLEU N-gram matching and named entities matching. Our team submitted three runs during the task evaluation period and they ranked number 11, 15 and 19 among the 90 participating systems according to the official Mean Pearson correlation metric for the task. We also report an unofficial run with mean Pearson correlation of 0.59221 on STS2013 test dataset, ranking as the 3rd best system among the 90 participating systems. c 2013 Association for Computational Linguistics

关键词： Semantics

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Language independent transliteration mining system using finite state automata framework 48

Language independent transliteration mining system using fin...

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2010 Named Entities Workshop, NEWS 2010 at the 48th Annual Meeting of the Association for Computational Linguistics, ACL 2010 - Proceedings of the Workshop

作者： Noeman, Sara Madkour, Amgad Human Language Technologies Group IBM Cairo Technology Development Center P.O.Box 166 El-Haram Giza Egypt

ISBN: (纸本)1932432787

We propose a Named Entities transliteration mining system using Finite State Automata (FSA). We compare the proposed approach with a baseline system that utilizes the Editex technique to measure the length-normalized phonetic based edit distance between the two words. We submitted three standard runs in NEWS2010 shared task and ranked first for English to Arabic (WM-EnAr) and obtained an F-measure of 0.915, 0.903, and 0.874 respectively. © 2010 Association for Computational Linguistics

关键词： Natural language processing systems

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An intelligent DSS to anticipate the impacts of wildcards on the tourism industry in Egypt

An intelligent DSS to anticipate the impacts of wildcards on...

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2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10

作者： Omran, Ahmed Saleh, Mohamed Agami, Nedaa El-Shishiny, Hisham Computer Science Department Faculty of Computers and Information Fayoum University Egypt Decision Support and Operation Research Department Faculty of Computers and Information Cairo University Egypt Advanced Technology IBM Cairo Technology Development Center Egypt

ISBN: (纸本)9781424481354

Research on the future of the Egyptian tourism industry has become the subject of countless studies and debates. Different wildcards have high impacts on this crucial industry and ultimately of the future of the Egyptian national income. This paper is about an Intelligent Decision Support System (DSS) that specifically focuses on assessing the impacts of wildcards on the future revenues of the Egyptian tourism industry. The developed DSS integrates both qualitative and quantitative DSS methods (called the enhanced Trend Impact Analysis and Real-Time Delphi Survey). Also, the developed analytical tool is used to generate various scenarios (not just a single forecast) by modifying the extrapolation of historical trends in view of tourism experts. © 2010 IEEE.

关键词： Decision support systems

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Language independent transliteration mining system using finite state automata framework 10

Language independent transliteration mining system using fin...

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Proceedings of the 2010 Named Entities Workshop

作者： Sara Noeman Amgad Madkour IBM Cairo Technology Development Center Giza Egypt

ISBN: (纸本)9781932432787

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An intelligent DSS to anticipate the impacts of wildcards on the tourism industry in Egypt

An intelligent DSS to anticipate the impacts of wildcards on...

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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Ahmed Omran Mohamed Saleh Nedaa Agami Hisham El-Shishiny Computer Science Department Faculty of Computers and Information Fayoum University Egypt Decision Support and Operation Research Department Faculty of Computers and Information Cairo University Egypt Advanced Technology and Center of Advanced Studies IBM Cairo Technology Development Center Egypt

关键词： Ontologies Decision support systems Knowledge acquisition Forecasting Visualization Industries Steady-state

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Wind flow modeling and simulation over the Giza Plateau cultural heritage site in Egypt

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Journal on Computing and Cultural Heritage 2009年第2期2卷 1–22页

作者： Hussein, Ashraf S. El-Shishiny, Hisham Faculty of Computer and Information Sciences Ain Shams University Egypt IBM Center for Advanced Studies in Cairo IBM Cairo Technology Development Center Egypt Branch Egypt

In this article, the wind flow over one of the most important Egyptian historical heritage sites, the Giza Plateau, was investigated using the Computational Fluid Dynamics (CFD) state-of-the-art techniques. The present study addresses the influences of wind flow structure, as an important denudation factor, on the site and its famous monuments: the Pyramids and the Great Sphinx. Three-dimensional CFD simulations have been performed based on the Reynolds Averaged Navier-Stokes (RANS) equations for the cases of the northwest wind, at the average wind speed over the year, and the southwest windstorms. In addition, the wind-driven sand was considered for the same cases. Particular attention was paid to the Great Sphinx and the Pyramids to investigate their parts most vulnerable to the wind, which is contributing to the erosion of these monuments. The Great Sphinx was buried by sand and it was cleared several times throughout its history. In this study, we also address the less understood, yet important, burial mechanism of the Great Sphinx. The present work may give more insight to the effect of wind around the Giza Plateau when developing a global plan for conserving and protecting the site. © 2009 ACM.

关键词： Computational fluid dynamics

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Language independent transliteration system using phrase based SMT approach on substrings

Language independent transliteration system using phrase bas...

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2009 Named Entities Workshop: Shared Task on Transliteration, NEWS 2009 at the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, ACL-IJCNLP 2009

作者： Noeman, Sara IBM Cairo Technology and Development Center Giza Egypt

Everyday the newswire introduce events from all over the world, highlighting new names of persons, locations and organizations with different origins. These names appear as Out of Vocabulary (OOV) words for Machine translation, cross lingual information retrieval, and many other NLP applications. One way to deal with OOV words is to transliterate the unknown words, that is, to render them in the orthography of the second language. We introduce a statistical approach for transliteration only using the bilingual resources released in the shared task and without any previous knowledge of the target languages. Mapping the Transliteration problem to the Machine Translation problem, we make use of the phrase based SMT approach and apply it on substrings of names. In the English to Russian task, we report ACC (Accuracy in top-1) of 0.545, Mean F-score of 0.917, and MRR (Mean Reciprocal Rank) of 0.596. Due to time constraints, we made a single experiment in the English to Chinese task, reporting ACC, Mean F-score, and MRR of 0.411, 0.737, and 0.464 respectively. Finally, it is worth mentioning that the system is language independent since the author is not aware of either languages used in the experiments. © 2009 ACL and AFNLP.

关键词： Machine translation

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Language independent transliteration system using phrase based SMT approach on substrings 09

Language independent transliteration system using phrase bas...

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Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration

作者： Sara Noeman IBM Cairo Technology & Development Center Giza Egypt

ISBN: (纸本)9781932432572

Everyday the newswire introduce events from all over the world, highlighting new names of persons, locations and organizations with different origins. These names appear as Out of Vocabulary (OOV) words for Machine translation, cross lingual information retrieval, and many other NLP applications. One way to deal with OOV words is to transliterate the unknown words, that is, to render them in the orthography of the second *** introduce a statistical approach for transliteration only using the bilingual resources released in the shared task and without any previous knowledge of the target languages. Mapping the Transliteration problem to the Machine Translation problem, we make use of the phrase based SMT approach and apply it on substrings of names. In the English to Russian task, we report ACC (Accuracy in top-1) of 0.545, Mean F-score of 0.917, and MRR (Mean Reciprocal Rank) of *** to time constraints, we made a single experiment in the English to Chinese task, reporting ACC, Mean F-score, and MRR of 0.411, 0.737, and 0.464 ***, it is worth mentioning that the system is language independent since the author is not aware of either languages used in the experiments.

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Machine learning methodologies in P300 speller Brain-Computer Interface systems

Machine learning methodologies in P300 speller Brain-Compute...

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National Conference on Radio Science (NSRC)

作者： Abeer E. Selim Manal Abdel Wahed Yasser M. Kadah IBM CTDC (Cairo Technology Development Center) Egypt Biomedical Engineering Department Cairo University Egypt Center for Informatics Science Nile University Egypt

Brain-computer interfaces (BCI) is a one kind of communication system that enables control of devices or communication with others only through brain signal activities without using motor activities. P300 Speller is a BCI paradigm that helps disabled subjects to spell words by means of their brain signal activities. This paper tries to demonstrate the performance of different machine learning algorithms based on classification accuracy. Performance has been evaluated on the data sets acquired using BCI2000's P300 Speller Paradigm provided by BCI competitions II (2003) & III (2004) organizers for three subjects. As a primary stage, a preprocessing was applied on the samples in order to extract the most significant features before introducing them to machine learning algorithms. The algorithms applied are Bayesian linear discriminant analysis (BLDA), linear support vector machine (SVM), Fisher linear discriminant analysis (FLDA), generalized Anderson's task linear classifier (GAT), linear discriminant analysis (LDA). BLDA and SVM yielded the highest accuracy for all 3 subjects. Principal component analysis (PCA) was applied on the data presented to the BLDA classifier for dimensionality reduction. The results obtained after this stage were slightly lower than those without reduction but they are still acceptable. This advantage could help in using P300 Speller BCI system beyond mere proof-of concept systems and allow their widespread application out of laboratories.

关键词： Machine learning Brain computer interfaces Linear discriminant analysis Machine learning algorithms Support vector machines Support vector machine classification Principal component analysis Communication system control Control systems Data mining

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