41726126
An Approach to Acquire Semantic Relationships Between Words from Web Document
In this paper, we focus on the semantic relationships acquisition from Chinese web documents motivated by the large requirement of web question answering system in e-Learning. With our scheme, we dwindle in numbers of text to be analyzed and obtain initial sentence-level text in pre-process phase. Then linguistic rules, which are broken down into unambiguous and ambiguous, designed for Chinese phrases are applied to these sentence-level text to extract the synonymy relationship, hyponymy relationship, hypernymy relationship and parataxis relationship. Lastly, candidates are refined using two heuristics. Compared to other previous works, we apply not only strict unambiguous linguistic rules but also loose ambiguous linguistic rules to extract relationships and proposed efficient approach to refine the outputs of these rules. Experiments show that this method can acquire semantic relationships efficiently and effectively.
27295065
Automatic extraction of titles from general documents using machine learning
In this paper, we propose a machine learning approach to title extraction from general documents. By general documents, we mean documents that can belong to any one of a number of specific genres, including presentations, book chapters, technical papers, brochures, reports, and letters. Previously, methods have been proposed mainly for title extraction from research papers. It has not been clear whether it could be possible to conduct automatic title extraction from general documents. As a case study, we consider extraction from Office including Word and PowerPoint. In our approach, we annotate titles in sample documents (for Word and PowerPoint, respectively) and take them as training data, train machine learning models, and perform title extraction using the trained models. Our method is unique in that we mainly utilize formatting information such as font size as features in the models. It turns out that the use of formatting information can lead to quite accurate extraction from general documents. Precision and recall for title extraction from Word are 0.810 and 0.837, respectively, and precision and recall for title extraction from PowerPoint are 0.875 and 0.895, respectively in an experiment on intranet data. Other important new findings in this work include that we can train models in one domain and apply them to other domains, and more surprisingly we can even train models in one language and apply them to other languages. Moreover, we can significantly improve search ranking results in document retrieval by using the extracted titles.
14292213
Title extraction from bodies of HTML documents and its application to web page retrieval
This paper is concerned with automatic extraction of titles from the bodies of HTML documents. Titles of HTML documents should be correctly defined in the title fields; however, in reality HTML titles are often bogus. It is desirable to conduct automatic extraction of titles from the bodies of HTML documents. This is an issue which does not seem to have been investigated previously. In this paper, we take a supervised machine learning approach to address the problem. We propose a specification on HTML titles. We utilize format information such as font size, position, and font weight as features in title extraction. Our method significantly outperforms the baseline method of using the lines in largest font size as title (20.9%-32.6% improvement in F1 score). As application, we consider web page retrieval. We use the TREC Web Track data for evaluation. We propose a new method for HTML documents retrieval using extracted titles. Experimental results indicate that the use of both extracted titles and title fields is almost always better than the use of title fields alone; the use of extracted titles is particularly helpful in the task of named page finding (23.1% -29.0% improvements).
28386762
Taxonomy Building and Machine Learning Based Automatic Classification for Knowledge-Oriented Chinese Questions
In this paper, we propose a taxonomy for knowledge-oriented question, and study the machine learning based classification for knowledge-oriented Chinese questions. By knowledge-oriented questions, we mean questions carrying information or knowledge about something, which cannot be well described by previous taxonomies. We build the taxonomy after the study of previous work and analysis of 6776 Chinese knowledge-oriented questions collected from different realistic sources. Then we investigate the new task of knowledge-oriented Chinese questions classification based on this taxonomy. In our approach, the popular SVM learning method is employed as classification algorithm. We explore different features and their combinations and different kernel functions for the classification, and use different performance metrics for evaluation. The results demonstrate that the proposed approach is desirable and robust. Thorough error analysis is also conduced.
16416421
Bayesian shape localization for face recognition using global and local textures
We present a fully automatic system for face recognition in databases, with only a small number of samples (even a single sample) for each individual. The shape localization problem is formulated in the Bayesian framework. In the learning stage, the RankBoost approach is introduced to model the likelihood of local features associated with the fiducial point, while preserving the prior ranking order between the ground truth position and its neighbors; in the inferring stage, a simple efficient iterative algorithm is proposed to uncover the MAP shape by locally modeling the likelihood distribution around each fiducial point. Based on the accurately located fiducial points, two popular mutual enhancing texture features for human face representation are automatically extracted and integrated: global texture features, which are the normalized shape-free gray-level values enclosed in the mean shape: local texture features, which are represented by Gabor wavelets extracted at the fiducial points (eye corners, mouth, etc.). Global texture mainly encodes the low-frequency information of a face, while local texture encodes the local high-frequency components. Extensive experiments illustrate that our proposed shape localization approach significantly improves the shape location accuracy, robustness, and face recognition rate; moreover, experiments conducted on the FERET and Yale databases show that our algorithm outperforms the classical eigenfaces and fisherfaces, as well as other approaches utilizing shape and global and local textures.
481550
Improving Fine-Grained Irregular Shared-Memory Benchmarks by Data Reordering
We demonstrate that data reordering can substantially improve the performance of fine-grained irregular shared-memory benchmarks, on both hardware and software shared-memory systems. In particular, we evaluate two distinct data reordering techniques that seek to co-locate in memory objects that are in close proximity in the physical system modeled by the computation. The effects of these techniques are increased spatial locality and reduced false sharing. We evaluate the effectiveness of the data reordering techniques on a set of five irregular applications from SPLASH-2 and Chaos. We implement both techniques in a small library, allowing us to enable them in an application by adding less than 10 lines of code. Our results on one hardware and two software shared-memory systems show that, with data reordering during initialization, the performance of these applications is improved by 12%-99% on the Origin 2000, 30%-366% on TreadMarks, and 14%-269% on HLRC.
6730998
Monitoring remotely executing shared memory programs in software DSMs
Peer-to-peer (P2P) cycle sharing over the Internet has become increasingly popular as a way to share idle cycles. A fundamental problem faced by P2P cycle sharing systems is how to incrementally monitor and verify, with low overhead, the execution of jobs submitted to a remote untrusted hosting machine, or cluster of machines. In this paper, we present the design and implementation of GripCop DSM, a novel incremental execution monitoring and verification scheme for software distributed shared memory (SDSM) programs running on remote clusters. Our scheme maximally leverages the shared memory abstraction provided by the SDSM system by extending the shared memory abstraction to the monitoring process by replicating one of the processes running on the host cluster to verify intermediate results at runtime. Our GripCop DSM employs two monitoring schemes: (i) a full-scale monitoring scheme that completely replicates the computation of a process running on the cluster; and (ii) a decoy monitoring scheme that deceives the host cluster into believing that full-scale monitoring is being performed without it ever actually being done, thereby incurring negligible overhead. Experiments show that the combined use of full-scale and decoy monitoring ensures faithful execution with low performance impact, even over a wide area network
16117857
Anycast in Locality-Aware Peer-to-Peer Overlay Networks
This paper advocates implementing anycast in peer-to-peer (p2p) overlay networks. We argue that anycast in p2p overlays (exemplified by Pastry, Tapestry, Chord, CAN) combines the advantages of IP anycast and existing application-layer anycast services. We show that anycast can leverage the locality-awareness embedded in existing p2p overlays. The locality-awareness of the p2p routing substrates can be extended to support anycast and anycast is achieved as the result of the enhanced locality-aware routing. We have implemented anycast in Pastry, and experiments confirm that with high probability, a message addressed to an anycast group is routed to the closest node in the group according to the proximity metric. We also evaluate the performance of anycast using a realistic failure trace and the results show that our implementation of anycast is resilient to node failures.
10673582
Automatic 3D reconstruction for face recognition
An analysis-by-synthesis framework for face recognition with variant pose, illumination and expression (PIE) is proposed in this paper. First, an efficient 2D-to-3D integrated face reconstruction approach is introduced to reconstruct a personalized 3D face model from a single frontal face image with neutral expression and normal illumination. Then, realistic virtual faces with different PIE are synthesized based on the personalized 3D face to characterize the face subspace. Finally, face recognition is conducted based on these representative virtual faces. Compared with other related works, this framework has the following advantages: 1) only one single frontal face is required for face recognition, which avoids the burdensome enrollment work; 2) the synthesized face samples provide the capability to conduct recognition under difficult conditions like complex PIE; and 3) the proposed 2D-to-3D integrated face reconstruction approach is fully automatic and more efficient. The extensive experimental results show that the synthesized virtual faces significantly improve the accuracy of face recognition with variant PIE.