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.
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 Also, the previous very high LSI results in the literature based on “mate-finding” were superseded by more realistic relevance-based evaluations; LSI performance proved comparable to that of other statistical corpus-based methods.
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.
30026565
A Web-based Real-time Cooperative Editor in Java
6411174
SMILAuthor: An Authoring System for SMIL-Based Multimedia Presentations
In this paper, an authoring tool named SMILAuthor for SMIL-based multimedia presentations is proposed. SMILAuthor adopts standard SMIL language as the format of the presentation to generate reusable and easily accessible presentations. Moreover, powerful editing functions such as cut, copy, and paste are supported by the system in a timeline-based manner. In order to support timeline-based editing functions, the playback duration of each object in the input SMIL script is first calculated by the parsing process of the system. The parsing process extracts and converts the temporal relationship of the input script to Real-Time Synchronization Model (RTSM), and the playback duration of each object in the script is then computed by traversing the RTSM. Editing results are converted to the SMIL format and saved in the output file. Language structure of SMIL is hidden by the system and the temporal information is visualized in the timeline manner to provide users an easy way to understand and control the timing of each object. Implementation of the system provides a friendly WYSIWYG environment and multiple views/windows are provided by the system to help authors compose multimedia presentations efficiently.
2153539
Prediction of Protein Subcellular Multi-locations with a Min-Max Modular Support Vector Machine
How to predict subcellular multi-locations of proteins with machine learning techniques is a challenging problem in computational biology community. Regarding the protein multi-location problem as a multi-label pattern classification problem, we propose a new predicting method for dealing with the protein subcellular localization problem in this paper. Two key points of the proposed method are to divide a seriously unbalanced multi-location problem into a number of more balanced two-class subproblems by using the part-versus-part task decomposition approach, and learn all of the subproblems by using the min-max modular support vector machine (M3-SVM). To evaluate the effectiveness of the proposed method, we perform experiments on yeast protein data set by using two kinds of task decomposition strategies and three kinds of feature extraction methods. The experimental results demonstrate that our method achieves the highest prediction accuracy, which is much better than that obtained by the existing approach based on the traditional support vector machine.
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.
12173372
Extracting Features from Protein Sequences Using Chinese Segmentation Techniques for Subcellular Localization
This paper proposes a new method for extracting features from protein sequences to deal with the problem of protein subcellular localization. The idea behind the method arises from Chinese segmentation techniques. We regard the amino acid sequences as text and segment them into words in a non-overlapping way. The words are predefined in a dictionary, which includes valuable words according to some criteria. Every word in the dictionary will be assigned a weight, and a matching strategy called maximum weight product is adopted for segmentation. By recording word frequencies, a given sequence can be converted into a feature vector. To evaluate the effectiveness of the proposed feature extraction method, two different kinds of classifiers are used to predict protein subcellular locations. The experimental results show that our method is superior to existing approaches in classification accuracy and reduces the number of dimensions of feature space at the same time.