295949
A Study of Approaches to Hypertext Categorization
Hypertext poses new research challenges for text classification. Hyperlinks, HTML tags, category labels distributed over linked documents, and meta data extracted from related Web sites all provide rich information for classifying hypertext documents. How to appropriately represent that information and automatically learn statistical patterns for solving hypertext classification problems is an open question. This paper seeks a principled approach to providing the answers. Specifically, we define five hypertext regularities which may (or may not) hold in a particular application domain, and whose presence (or absence) may significantly influence the optimal design of a classifier. Using three hypertext datasets and three well-known learning algorithms (Naive Bayes, Nearest Neighbor, and First Order Inductive Learner), we examine these regularities in different domains, and compare alternative ways to exploit them. Our results show that the identification of hypertext regularities in the data and the selection of appropriate representations for hypertext in particular domains are crucial, but seldom obvious, in real-world problems. We find that adding the words in the linked neighborhood to the page having those links (both inlinks and outlinks) were helpful for all our classifiers on one data set, but more harmful than helpful for two out of the three classifiers on the remaining datasets. We also observed that extracting meta data from related Web sites was extremely useful for improving classification accuracy in some of those domains. Finally, the relative performance of the classifiers being tested provided insights into their strengths and limitations for solving classification problems involving diverse and often noisy Web pages.
10555290
From Lasso regression to Feature vector machine
Lasso regression tends to assign zero weights to most irrelevant or redundant features, and hence is a promising technique for feature selection. Its limitation, however, is that it only offers solutions to linear models. Kernel machines with feature scaling techniques have been studied for feature selection with non-linear models. However, such approaches require to solve hard non-convex optimization problems. This paper proposes a new approach named the Feature Vector Machine (FVM). It reformulates the standard Lasso regression into a form isomorphic to SVM, and this form can be easily extended for feature selection with non-linear models by introducing kernels defined on feature vectors. FVM generates sparse solutions in the nonlinear feature space and it is much more tractable compared to feature scaling kernel machines. Our experiments with FVM on simulated data show encouraging results in identifying the small number of dominating features that are non-linearly correlated to the response, a task the standard Lasso fails to complete.
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.
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.
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.
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.
2958357
An Architecture and the Related Mechanisms for Web-based Global Cooperative Teamwork Support
Given the exposure of the Internet and the Web, there is a significant impact on Web-based cooperative teamwork support which can be beneficial to many teamwork managers and normal team members who may be either computing or non-computing professionals. In this paper, we focus on our research into a more effective architecture for Web-based teamwork automation support and its corresponding innovative mechanisms for various perspectives including visual process modelling for teamwork managers and process enactment for team members in an asynchronous and synchronous manner. Our research prototype is implemented in Java and data repository used can be an object-oriented or relational database.
4917474
Effective Visualisation of Workflow Enactment
Although most existing teamwork management systems support user-friendly interface to some extent, few of them have take into consideration of the special requirements of workflow visualisation. This paper realises the unique features of visualisation for run-time workflow, i.e., workflow enactment and execution. We present a detailed discussion of the emerging problems against the general aesthetic criteria for drawing the workflow layout. In order to support most essential workflow enactment facilities, the following three mechanisms are provided. Firstly, Sugiyama algorithm has been systematically incorporated into our prototype to create well structured workflow layout initially. Secondly, when the workflow process dynamically changes, we can adjust workflow layout by our force-scan algorithm to retain the mental maps created earlier among team members. Thirdly, we have also applied the technique of the fisheye view to offer a context focus mechanism for workflow users and to utilise the screen size more effectively. With these visualisation techniques, a better atmosphere may be facilitated for teamwork.
14477293
To Select or To Weigh: A Comparative Study of Model Selection and Model Weighing for SPODE Ensembles
An ensemble of Super-Parent-One-Dependence Estimators (SPODEs) offers a powerful yet simple alternative to naive Bayes classifiers, achieving significantly higher classification accuracy at a moderate cost in classification efficiency. Currently there exist two families of methodologies that ensemble candidate SPODEs for classification. One is to select only helpful SPODEs and uniformly average their probability estimates, a type of model selection. Another is to assign a weight to each SPODE and linearly combine their probability estimates, a methodology named model weighing. This paper presents a theoretical and empirical study comparing model selection and model weighing for ensembling SPODEs. The focus is on maximizing the ensemble's classification accuracy while minimizing its computational time. A number of representative selection and weighing schemes are studied, providing a comprehensive research on this topic and identifying effective schemes that provide alternative trades-off between speed and expected error.
14000419
Using an experimental study to develop group awareness support for real-time distributed collaborative writing
Supporting group awareness is vital for the success of real-time, distributed, collaborative writing systems. Many awareness mechanisms have been introduced, but highly effective solutions are few. The research presented in this paper focuses on the development of awareness mechanisms using an experimental study of synchronous distributed collaborative writing. Our study has made two major contributions to research on group awareness. First, the study compares the importance of different awareness elements in supporting group awareness for collaborative writing. The results of our Wilcoxon test on awareness elements identify the five most important elements, including ‘‘Being able to comment on what other users have done,’’ ‘‘Knowing what actions other users are currently taking,’’ ‘‘Providing a communication tool when audio is not available,’’ ‘‘Knowing other user’s working areas in the document,’’ and ‘‘Knowing other user’s tasks.’’ Second, the research proposes mechanisms corresponding to the above-mentioned five awareness elements. The mechanisms include Dynamic Task List (DTL), Modification Director (MD), Advanced Chat (AC) and Split Window View (SWV). These mechanisms provide support for various aspects of group awareness, and add many enhanced features to existing awareness mechanisms. For example, DTL presents high-level information about authors’ responsibilities and the correlation between their work allocations. MD notifies users instantaneously whenever their work is modified by other authors. AC enhances communication between users by allowing them to attach document objects such as text and diagrams to a conversation message. And, SWV provides the views of other authors’ working areas and viewing areas simultaneously. � 2005 Elsevier B.V. All rights reserved.
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.