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 Also, the previous very high LSI results in the literature based on “mate-finding” were superseded by more realistic relevance-based evaluations; LSI performance proved comparable to that of other statistical corpus-based methods.
14593921
Stochastic link and group detection
Link detection and analysis has long been important in the social sciences and in the government intelligence community. A significant effort is focused on the structural and functional analysis of "known" networks. Similarly, the detection of individual links is important but is usually done with techniques that result in "known" links. More recently the internet and other sources have led to a flood of circumstantial data that provide probabilistic evidence of links. Co-occurrence in news articles and simultaneous travel to the same location are two examples.We propose a probabilistic model of link generation based on membership in groups. The model considers both observed link evidence and demographic information about the entities. The parameters of the model are learned via a maximum likelihood search. In this paper we describe the model and then show several heuristics that make the search tractable. We test our model and optimization methods on synthetic data sets with a known ground truth and a database of news articles.
7913579
Robustness of adaptive filtering methods in a cross-benchmark evaluation
This paper reports a cross-benchmark evaluation of regularized logistic regression (LR) and incremental Rocchio for adaptive filtering. Using four corpora from the Topic Detection and Tracking (TDT) forum and the Text Retrieval Conferences (TREC) we evaluated these methods with non-stationary topics at various granularity levels, and measured performance with different utility settings. We found that LR performs strongly and robustly in optimizing T11SU (a TREC utility function) while Rocchio is better for optimizing Ctrk (the TDT tracking cost), a high-recall oriented objective function. Using systematic cross-corpus parameter optimization with both methods, we obtained the best results ever reported on TDT5, TREC10 and TREC11. Relevance feedback on a small portion (0.05~0.2%) of the TDT5 test documents yielded significant performance improvements, measuring up to a 54% reduction in Ctrk and a 20.9% increase in T11SU (with b=0.1), compared to the results of the top-performing system in TDT2004 without relevance feedback information.
17079123
Applying CLIR Techniques to Event Tracking
Cross-lingual event tracking from a very large number of information sources (thousands of Web sites, for example) is an open challenge. In this paper we investigate effective and scalable solutions for this problem, focusing on the use of cross-lingual information retrieval techniques to translate a small subset of the training documents, as an alternative to the conventional approach of translating all the multilingual test documents. In addition, we present a new variant of weighted pseudo-relevance feedback for adaptive event tracking. This new method simplifies the assumption and the computation in the best-known approach of this kind, yielding a better result than the latter on benchmark datasets in our evaluations.
15260186
Using recursive classification to discover predictive features
Finding most predictive features for statistical classification is a challenging problem and has important applications. Support Vector Machines (SVMs), for example, have been found successful with a recursive procedure in selecting most important genes for cancer prediction. It is not well understood, however, how much the success depends on the choice of the classifier, and how much on the recursive procedure. We answer this question by examining multiple classifiers (SVM, ridge regression and Rocchio) with feature selection in recursive and non-recursive settings, on a DNA microarray dataset (AMLALL) and a text categorization benchmark (Reuters-21578). We found recursive ridge regression most effective: its best classification performance (zero error) on the AMLALL dataset was obtained when using only 3 genes (selected from over 7000), which is more impressive than the best published result on the same benchmark - zero error of recursive SVM using 8 genes. On Reuters-21578, recursive ridge regression also achieves the best result ever published (the improvement was verified in a significance test). An in-depth analysis of the experimental results shows that the choice of classifier heavily influences the recursive feature selection process: the ridge regression classifier tends to penalize redundant features to a much larger extent than the SVM does.
13451873
The Enron Corpus: A New Dataset for Email Classification Research
Automated classification of email messages into user-specific folders and information extraction from chronologically ordered email streams have become interesting areas in text learning research. However, the lack of large benchmark collections has been an obstacle for studying the problems and evaluating the solutions. In this paper, we introduce the Enron corpus as a new test bed. We analyze its suitability with respect to email folder prediction, and provide the baseline results of a state-of-the-art classifier (Support Vector Machines) under various conditions, including the cases of using individual sections (From, To, Subject and body) alone as the input to the classifier, and using all the sections in combination with regression weights.
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.
11919242
Detecting and Classifying Emotion in Popular Music
Music expresses emotion. However, analyzing the emotion in music by computer is a difficult task. Some work can be found in the literature, but the results are not satisfactory. In this paper, an emotion detection and classification system for pop music is presented. The system extracts feature values from the training music files by PsySound2 and generates a music model from the resulting feature dataset by a classification algorithm. The model is then used to detect the emotion perceived in music clips. To further improve the classification accuracy, we evaluate the significance of each music feature and remove the insignificant features. The system uses a database of 195 music clips to enhance reliability and robustness.
4910050
Empirical Study on Benchmarking Software Development Tasks
Benchmarking is one of the most important methods to learn the best practices for software process improvement. However, in current software process context, benchmarking is mainly for projects rather than software development tasks. Can we benchmark software development tasks? If so, how to? Moreover, benchmarking software development tasks has to deal with multivariate and variable return to scale (VRS). This paper reports practical experience of benchmarking software development tasks under multivariate and VRS constraints using Data Envelopment Analysis (DEA). The analysis of experience data in Institute of Software, Chinese Academy of Sciences (ISCAS) indicates that the ideas and techniques of benchmarking software projects can be deployed at the software development task level. Moreover, results also show that DEA VRS model allows the developers to gain new insight about how to identify the relatively efficient tasks as the task performance benchmark and how to establish different reference sets for each relatively inefficient task under multivariate and VRS constraints. We thus recommend DEA VRS model be used as the default technique for appropriately benchmarking software development tasks. Our results are beneficial to software process improvement. To the best of our knowledge, we believe that it is the first time to report such comprehensive and repeatable results of benchmarking software development tasks using DEA.
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.
4427518
On the stability of rational, heterogeneous interdomain route selection
The recent discovery of instability caused by the interaction of local routing policies of multiple ASes has led to extensive research on the subject. However, previous studies analyze stability under a specific route selection algorithm. In this paper, instead of studying a specific route selection algorithm, we study a general class of route selection algorithms which we call rational route selection algorithms. We present a sufficient condition to guarantee routing convergence in a heterogeneous network where each AS runs any rational route selection algorithm. Applying our general results, we study the potential instability of a network where the preference of an AS depends on not only its egress routes to the destinations but also its inbound traffic patterns (i.e., the distribution of incoming traffic from its neighbors). We show that there exist networks which will have persistent route oscillations even when the ASes strictly follow the constraints imposed by business considerations, and adopt any rational route selection algorithms.