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.
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.
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.
306973
A Loss Function Analysis for Classification Methods in Text Categorization
This paper presents a formal analysis of popular text classification methods, focusing on their loss functions whose minimization is essential to the optimization of those methods, and whose decomposition into the training-set loss and the model complexity enables cross-method comparisons on a common basis from an optimization point of view. Those methods include Support Vector Machines, Linear Regression, Logistic Regression, Neural Network, Naive Bayes, K-Nearest Neighbor, Rocchio-style and Multi-class Prototype classifiers. Theoretical analysis (including our new derivations) is provided for each method, along with evaluation results for all the methods on the Reuters-21578 benchmark corpus. Using linear regression, neural networks and logistic regression methods as examples, we show that properly tuning the balance between the training-set loss and the complexity penalty would have a significant impact to the performance of a classifier. In linear regression, in particular, the tuning of the complexity penalty yielded a result (measured using macro-averaged F1) that outperformed all text categorization methods ever evaluated on that benchmark corpus, including Support Vector Machines.
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.
16041292
Expert network: effective and efficient learning from human decisions in text categorization and retrieval
Expert Network (ExpNet) is our new approach to automatic categorization and retrieval of natural language texts. We use a training set of texts with expert-assigned categories to construct a network which approximately reflects the conditional probabilities of categories given a text. The input nodes of the network are words in the training texts, the nodes on the intermediate level are the training texts, and the output nodes are categories. The links between nodes are computed based on statistics of the word distribution and the category distribution over the training set. ExpNet is used for relevance ranking of candidate categories of an arbitrary text in the case of text categorization, and for relevance ranking of documents via categories in the case of text retrieval. We have evaluated ExpNet in categorization and retrieval on a document collection of the MEDLINE database, and observed a performance in recall and precision comparable to the Linear Least Squares Fit (LLSF) mapping method, and significantly better than other methods tested. Computationally, ExpNet has an O(N 1og N) time complexity which is much more efficient than the cubic complexity of the LLSF method. The simplicity of the model, the high recall-precision rates, and the efficient computation together make ExpNet preferable as a practical solution for real-world applications.
1344510
COPE: traffic engineering in dynamic networks
Traffic engineering plays a critical role in determining the performance and reliability of a network. A major challenge in traffic engineering is how to cope with dynamic and unpredictable changes in traffic demand. In this paper, we propose COPE, a class of traffic engineering algorithms that optimize for the expected scenarios while providing a worst-case guarantee for unexpected scenarios. Using extensive evaluations based on real topologies and traffic traces, we show that COPE can achieve efficient resource utilization and avoid network congestion in a wide variety of scenarios.
5091874
Integrating Web Services into Ontology-Based Web Portal
With the explosive emerged Web services, the Web becomes a world of information and applications. So portals whose goal is presenting a structured view onto the Web should provide users with a single point access to multiple types of not only information but also services. Recently, many systems have been developed using ontology-driven approaches to automatically generate and manage Web portals. However, all of them lack the integration of Web services. In this paper, we describe SPortS, an OWL-DL(Web Ontology Language-Description Logics) based portal generation system that integrates semantic Web services into generated portals at the presentation level, the data level and the function level. We present the portal generation process, the query decomposition and the service recommendation algorithms through which semantic Web services are integrated tightly into the generated portal.
43601007
Proportional k-Interval Discretization for Naive-Bayes Classifiers
This paper argues that two commonly-used discretization approaches, fixed k-interval discretization and entropy-based discretization have sub-optimal characteristics for naive-Bayes classification. This analysis leads to a new discretization method, Proportional k-Interval Discretization (PKID), which adjusts the number and size of discretized intervals to the number of training instances, thus seeks an appropriate trade-off between the bias and variance of the probability estimation for naive-Bayes classifiers. We justify PKID in theory, as well as test it on a wide cross-section of datasets. Our experimental results suggest that in comparison to its alternatives, PKID provides naive-Bayes classifiers competitive classification performance for smaller datasets and better classification performance for larger datasets.
9199797
Error Detection and Impact-Sensitive Instance Ranking in Noisy Datasets
Given a noisy dataset, how to locate erroneous instances and attributes and rank suspicious instances based on their impacts on the system performance is an interesting and important research issue. We provide in this paper an Error Detection and Impact-sensitive instance Ranking (EDIR) mechanism to address this problem. Given a noisy dataset D, we first train a benchmark classifier T from D. The instances, that cannot be effectively classified by T are treated as suspicious and forwarded to a subset S. For each attribute Ai, we switch Ai and the class label C to train a classifier APi for Ai. Given an instance Ik in S, we use APi and the benchmark classifier T to locate the erroneous value of each attribute Ai. To quantitatively rank instances in S, we define an impact measure based on the Information-gain Ratio (IR). We calculate IRi between attribute Ai and C, and use IRi as the impact-sensitive weight of Ai. The sum of impact-sensitive weights from all located erroneous attributes of Ik indicates its total impact value. The experimental results demonstrate the effectiveness of our strategies.
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.