14823403
Recognizing names in biomedical texts: a machine learning approach
MOTIVATION
With an overwhelming amount of textual information in molecular biology and biomedicine, there is a need for effective and efficient literature mining and knowledge discovery that can help biologists to gather and make use of the knowledge encoded in text documents. In order to make organized and structured information available, automatically recognizing biomedical entity names becomes critical and is important for information retrieval, information extraction and automated knowledge acquisition.


RESULTS
In this paper, we present a named entity recognition system in the biomedical domain, called PowerBioNE. In order to deal with the special phenomena of naming conventions in the biomedical domain, we propose various evidential features: (1) word formation pattern; (2) morphological pattern, such as prefix and suffix; (3) part-of-speech; (4) head noun trigger; (5) special verb trigger and (6) name alias feature. All the features are integrated effectively and efficiently through a hidden Markov model (HMM) and a HMM-based named entity recognizer. In addition, a k-Nearest Neighbor (k-NN) algorithm is proposed to resolve the data sparseness problem in our system. Finally, we present a pattern-based post-processing to automatically extract rules from the training data to deal with the cascaded entity name phenomenon. From our best knowledge, PowerBioNE is the first system which deals with the cascaded entity name phenomenon. Evaluation shows that our system achieves the F-measure of 66.6 and 62.2 on the 23 classes of GENIA V3.0 and V1.1, respectively. In particular, our system achieves the F-measure of 75.8 on the "protein" class of GENIA V3.0. For comparison, our system outperforms the best published result by 7.8 on GENIA V1.1, without help of any dictionaries. It also shows that our HMM and the k-NN algorithm outperform other models, such as back-off HMM, linear interpolated HMM, support vector machines, C4.5, C4.5 rules and RIPPER, by effectively capturing the local context dependency and resolving the data sparseness problem. Moreover, evaluation on GENIA V3.0 shows that the post-processing for the cascaded entity name phenomenon improves the F-measure by 3.9. Finally, error analysis shows that about half of the errors are caused by the strict annotation scheme and the annotation inconsistency in the GENIA corpus. This suggests that our system achieves an acceptable F-measure of 83.6 on the 23 classes of GENIA V3.0 and in particular 86.2 on the "protein" class, without help of any dictionaries. We think that a F-measure of 90 on the 23 classes of GENIA V3.0 and in particular 92 on the "protein" class, can be achieved through refining of the annotation scheme in the GENIA corpus, such as flexible annotation scheme and annotation consistency, and inclusion of a reasonable biomedical dictionary.


AVAILABILITY
A demo system is available at http://textmining.i2r.a-star.edu.sg/NLS/demo.htm. Technology license is available upon the bilateral agreement.
2536406
Improving Pronoun Resolution Using Statistics-Based Semantic Compatibility Information
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16665932
Document Flattening through Grid Modeling and Regularization
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14312797
Correcting document image warping based on regression of curved text lines
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15710341
Motion deblurring for optical character recognition
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38236882
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17502983
Motivating open source software developers: influence of transformational and transactional leaderships
Open Source Software (OSS) is developed by geographically distributed unpaid programmers. The success of such a seemingly chaotic OSS project will largely depend on how the project leader organizes and motivates the developers to contribute. Grounded on leadership and motivation theories, we proposed and tested a research model that seeks to explain the behavioral effects of a leader on the developers' motivation to contribute. Survey data collected from 118 OSS developers on Sourceforge.net was used to test the research model. The results indicate that leaders' transformational leadership is positively related to developers' intrinsic motivation and leaders' active management by exception, a form of transactional leadership, is positively related to developers' extrinsic motivation.
5965390
The use of bigrams to enhance text categorization
In this paper, we present an efficient text categorization algorithm that generates bigrams selectively by looking for ones that have an especially good chance of being useful. The algorithm uses the information gain metric, combined with various frequency thresholds. The bigrams, along with unigrams, are then given as features to two different classifiers: Naive Bayes and maximum entropy. The experimental results suggest that the bigrams can substantially raise the quality of feature sets, showing increases in the break-even points and F1 measures. The McNemar test shows that in most categories the increases are very significant. Upon close examination of the algorithm, we concluded that the algorithm is most successful in correctly classifying more positive documents, but may cause more negative documents to be classified incorrectly.
11495874
A new electronic cash model
Credit card is currently the most popular electronic payment method on the Internet, but it is not an ideal method from the security and privacy point of view. Electronic cash solves many inherent problems of credit card, such as anonymity and off-line payment, and is widely accepted as an ideal electronic payment method. However, there are issues that are yet to be satisfactorily solved, such as fair traceability and double-spending prevention of off-line schemes. In this paper, we present a customer generated electronic cash model that offers unique trade-off between credit card and traditional off-line electronic cash systems. This model provides anonymity for customers and is technically more secure than credit card systems. It eliminates the withdrawal phase of electronic cash systems, thus it is more convenient for customers to use. In addition, fair traceability is provided in case crimes take place.
29726361
Key Substitution Attacks on Some Provably Secure Signature Schemes
39519625
A CCA2 Secure Key Encapsulation Scheme Based on 3rd Order Shift Registers
In 1998, Cramer and Shoup proposed the first practical and provable cryptosystem against adaptive chosen ciphertext attack under the standard assumption in the standard model, that is, decisional Diffie-Hellman assumption. Recently, Lucks extended the Cramer-Shoup cryptosystem to a group of quadratic residues modulo a composite number and showed that the scheme is provably secure in the standard model. In this paper, we extend Lucks' key encapsulation scheme to a third order linear feedback shift register and is based on a new assunmption which is called shift register based decisional Diffie-Hellman assumptions (SR-DDH). The proposed scheme is provably secure against adaptive chosen ciphertext attack based on the hardness of shift register based decisional Diffie-Hellman assumption in the standard model and not in random oracle model. Furthermore, the size of public key and ciphertext are shorter than Cramer-Shoup cryptosystem and the computational complexity is also more efficient than Cramer-Shoup cryptosystem and Lucks scheme.