30891792
Cross-Lingual Pseudo-Relevance Feedback Using a Comparable Corpus
We applied a Cross-Lingual PRF (Pseudo-Relevance Feedback) system to both the monolingual task and the German->English task. We focused on the effects of extracting a comparable corpus from the given newspaper data; our corpus doubled the average precision when used together with a parallel corpus made available to participants. The PRF performance was lower for the queries with few relevant documents. We also examined the effects of the PRF first-step retrieval in the parallel corpus vs. the entire document collection.
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.
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.
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.
16851151
UMR: a multi-round algorithm for scheduling divisible workloads
In this paper we present an algorithm for scheduling parallel divisible workload applications. Our algorithm uses multiple rounds to overlap communication and computation between a master and several workers. We use "uniform" rounds, i.e. a fixed amount of work is sent out to all workers at each round. This restriction makes it possible to compute an approximately optimal number of rounds, which was not possible for previously proposed algorithms. In addition, we use more realistic platform models than those used in previous works. We provide an analysis of our algorithm both for homogeneous and heterogeneous platforms and present simulation results.
8861403
Design and implementation of the just-in-time retrieving policy for schedule-based distributed multimedia presentations
In order to provide the smooth playback of a distributed multimedia presentation, the object-retrieving engine for the player must fetch each object before its playback time. In this paper, a smart object-retrieving engine is proposed, which adopts a retrieving policy named the just-in-time policy. The policy expects the retrieval process of an object to finish right before the playback time of the object to achieve a better buffer utilization and network bandwidth efficiency. The proposed object-retrieving engine focuses on SMIL1.0-based multimedia presentations. By converting the synchronization relationship of objects in the SMIL 1.0 document to Real-Time Synchronization Model, which simplifies the handling of the synchronization relationship, and considering the end-to-end bandwidth as well as the user interactions, the object-retrieving engine determines the object request time for each object. The engine issues the request to fetch each object for the ongoing presentation at the object request time, and provides the player with proper media objects. The feasibility and better performance of the proposed just-in-time retrieving policy had been proved by performance measurements of system implementation.
11120680
Semantic mapping with multi-agent systems
This paper introduces a multi-agent system (MAS) architecture and a relevant algorithm for multiple agents to manage and deploy ontologies in a dynamic environment. Firstly, an ontology description is given followed by a three level conceptual model which shows the background of our work. Then a MAS architecture is presented. Finally, we demonstrate how this approach enables ontology evolution and integration by using the JADE platform.
18180906
HKUST/MTS: A Very Large Scale Mandarin Telephone Speech Corpus
The paper describes the design, collection, transcription and analysis of 200 hours of HKUST Mandarin Telephone Speech Corpus (HKUST/MTS) from over 2100 Mandarin speakers in mainland China under the DARPA EARS framework. The corpus includes speech data, transcriptions and speaker demographic information. The speech data include 1206 ten-minute natural Mandarin conversations between either strangers or friends. Each conversation focuses on a single topic. All calls are recorded over public telephone networks. All calls are manually annotated with standard Chinese characters (GBK) as well as specific mark-ups for spontaneous speech. A file with speaker demographic information is also provided. The corpus is the largest and first of its kind for Mandarin conversational telephone speech, providing abundant and diversified samples for Mandarin speech recognition and other application-dependent tasks, such as topic detection, information retrieval, keyword spotting, speaker recognition, etc. In a 2004 evaluation test by NIST, the corpus is found to improve system performance quite significantly.
6183487
Practical routing and torus assignment for RDT
The connection topology of interconnection networks and the routing algorithm often dominate system performance in MPCs. RDT has already been proved to be one of the best interconnection networks used in such systems. It supports a smaller diameter and degree than most at the size of one thousand to tens of thousands nodes. The floating vector routing algorithm is very effective for transferring messages, which also shows good performance. In this paper, a descending vector routing algorithm is proposed. We also proposed a practical torus assignment named RDT(2,2,1)/a and the deadlock-free routing algorithm. RDT(2,2,1)/a mainly focuses on the MPC that has about one thousand nodes. With a simplified simulation and comparison, the results show that RDT(2,2,1)/a has a better performance at the size of one thousand nodes.