8555900
Unsupervised Learning of Arabic Stemming Using a Parallel Corpus
This paper presents an unsupervised learning approach to building a non-English (Arabic) stemmer. The stemming model is based on statistical machine translation and it uses an English stemmer and a small (10 K sentences) parallel corpus as its sole training resources. No parallel text is needed after the training phase. Monolingual, unannotated text can be used to further improve the stemmer by allowing it to adapt to a desired domain or genre. Examples and results will be given for Arabic, but the approach is applicable to any language that needs affix removal. Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component. Task-based evaluation using Arabic information retrieval indicates an improvement of 22-38% in average precision over unstemmed text, and 96% of the performance of the proprietary stemmer above.
7098682
High-performing feature selection for text classification
This paper reports a controlled study on a large number of filter feature selection methods for text classification. Over 100 variants of five major feature selection criteria were examined using four well-known classification algorithms: a Naive Bayesian (NB) approach, a Rocchio-style classifier, a k-nearest neighbor (kNN) method and a Support Vector Machine (SVM) system. Two benchmark collections were chosen as the testbeds: Reuters-21578 and small portion of Reuters Corpus Version 1 (RCV1), making the new results comparable to published results. We found that feature selection methods based on chi2 statistics consistently outperformed those based on other criteria (including information gain) for all four classifiers and both data collections, and that a further increase in performance was obtained by combining uncorrelated and high-performing feature selection methods.The results we obtained using only 3% of the available features are among the best reported, including results obtained with the full feature set.
1743726
Hypertext Categorization using Hyperlink Patterns and Meta Data
Hypertext poses new text classi cation research challenges as hyperlinks, content of linked documents, and meta data about related web sites all provide richer sources of information for hypertext classi cation that are not available in traditional text classi cation. We investigate the use of such information for representing web sites, and the e ectiveness of di erent classi ers (Naive Bayes, Nearest Neighbor, and Foil) in exploiting those representations. We nd that using words in web pages alone often yields suboptimal performance of classi ers, compared to exploiting additional sources of information beyond document content. On the other hand, we also observe that linked pages can be more harmful than helpful when the linked neighborhoods are highly \noisy" and that links have to be used in a careful manner. More importantly, our investigation suggests that meta data which is often available, or can be acquired using Information Extraction techniques, can be extremely useful for improving classi cation accuracy. Finally, the relative performance of the di erent classi ers being tested gives us insights into the strengths and limitations of our algorithms for hypertext classi cation.
1409762
A scalability analysis of classifiers in text categorization
Real-world applications of text categorization often require a system to deal with tens of thousands of categories defined over a large taxonomy. This paper addresses the problem with respect to a set of popular algorithms in text categorization, including Support Vector Machines, k-nearest neighbor, ridge regression, linear least square fit and logistic regression. By providing a formal analysis of the computational complexity of each classification method, followed by an investigation on the usage of different classifiers in a hierarchical setting of categorization, we show how the scalability of a method depends on the topology of the hierarchy and the category distributions. In addition, we are able to obtain tight bounds for the complexities by using the power law to approximate category distributions over a hierarchy. Experiments with kNN and SVM classifiers on the OHSUMED corpus are reported on, as concrete examples.
3009196
Translingual Information Retrieval: Learning from Bilingual Corpora
Abstract Translingual information retrieval (TLIR) consists of providing a query in one language and searching document collections in one or more different languages. This paper introduces new TLIR methods and reports on comparative TLIR experiments with these new methods and with previously reported ones in a realistic setting. Methods fall into two categories: query translation and statistical-IR approaches establishing translingual associations. The results show that using bilingual corpora for automated extraction of term equivalences in context outperforms dictionarybased methods. Translingual versions of the Generalized Vector Space Model (GVSM) and Latent Semantic Indexing (LSI) perform well, as does translingual pseudo-relevance feedback (PRF) and Example-Based Term-in-context translation (EBT). All showed relatively small performance loss between monolingual and translingual versions, ranging between 87–101% of monolingual IR performance. Query translation based on a general machine-readable bilingual dictionary—heretofore the most popular method—did not match the performance of other, more sophisticated methods. Also, the previous very high LSI results in the literature based on “mate-finding” were superseded by more realistic relevance-based evaluations; LSI performance proved comparable to that of other statistical corpus-based methods.
14181364
Probabilistic score estimation with piecewise logistic regression
Well-calibrated probabilities are necessary in many applications like probabilistic frameworks or cost-sensitive tasks. Based on previous success of asymmetric Laplace method in calibrating text classifiers' scores, we propose to use piecewise logistic regression, which is a simple extension of standard logistic regression, as an alternative method in the discriminative family. We show that both methods have the flexibility to be piecewise linear functions in log-odds, but they are based on quite different assumptions. We evaluated asymmetric Laplace method, piecewise logistic regression and standard logistic regression over standard text categorization collections (Reuters-21578 and TRECAP) with three classifiers (SVM, Naive Bayes and Logistic Regression Classifier), and observed that piecewise logistic regression performs significantly better than the other two methods in the log-loss metric.
58339956
IPsec/VPN security policy correctness and assurance
With IPsec/VPN policies being widely deployed, how to correctly specify and configure them is critical in enforcing security requirements, especially among different administrative domains across the Internet. Under current practice, IPsec/VPN policies are specified individually by system administrators from different organizations without any formal coordination. This practice implies unintentional errors due to inconsistent IPsec/VPN policies. Furthermore, Internet routing dynamics may possibly interfere with IPsec/VPN policies such that unexpected conflicts occur due to a mismatch between the routing and IPsec/VPN layers. To deal with these problems, we formally define IPsec security requirements, policies, and their correctness criteria. Based on these definitions, we present an inter-domain architecture to automatically generate correct and efficient security policies. Our approach works when we are given a set of security requirements for a single end-to-end traffic flow. We can also deal with changes when new security requirements are added. Finally, we present simulation results which evaluate the performance of our solutions.
43250103
An Agent-Based Ontological Approach for Information Reorganisation
This paper presents a novel approach for information reorganisation which is particularly suitable for distributed computing, such as Web services. An agent-based architecture is developed to cope with service agents' on and off and provide semantic support for implementation of complex e-business applications that may span diverse enterprises. A frame-based ontology representation is discussed. The representation is well suited for representing complex relationships that are unavailable in current ontology related work. Our approach aims to support dynamic information reorganisation in the Internet based e-business applications. The obtained information can serve for the knowledge acquisition purpose.
920559
Verifiable distributed oblivious transfer and mobile agent security
The mobile agent is a fundamental building block of the mobile computing paradigm. In mobile agent security, oblivious transfer (OT) from a trusted party can be used to protect the agent's privacy and the hosts' privacy. In this paper, we introduce a new cryptographic primitive called Verifiable Distributed Oblivious Transfer (VDOT), which allows us to replace a single trusted party with a group of threshold trusted servers. The design of VDOT uses two novel techniques, consistency verification of encrypted secret shares and consistency verification through re-randomization. VDOT protects the privacy of both the sender and the receiver against malicious attacks of the servers. We also show the design of a system to apply VDOT to protect the privacy of mobile agents. Our design partitions an agent into the general portion and the security-sensitive portion. We also implement the key components of our system. As far as we know, this is the first effort to implement a system that protects the privacy of mobile agents. Our preliminary evaluation shows that protecting mobile agents not only is possible, but also can be implemented efficiently.
1856845
Typing evolving ambients
This paper presents a type system that deals with the type evolution problem of the ambient calculus. It breaks the law that the open capability must introduce to the opening process the exact type information of the opened ambient. It can also serve as a general type architecture supporting type evolution. A typical usage of the type system is illustrated in the immobile server example.
9864159
Harmonic Skeleton Guided Evaluation of Stenoses in Human Coronary Arteries
This paper presents a novel approach that three-dimensionally visualizes and evaluates stenoses in human coronary arteries by using harmonic skeletons. A harmonic skeleton is the center line of a multi-branched tubular surface extracted based on a harmonic function, which is the solution of the Laplace equation. This skeletonization method guarantees smoothness and connectivity and provides a fast and straightforward way to calculate local cross-sectional areas of the arteries, and thus provides the possibility to localize and evaluate coronary artery stenosis, which is a commonly seen pathology in coronary artery disease.