4391987
Automatic extraction of titles from general documents using machine learning
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 is 0.810 and 0.837 respectively, and precision and recall for title extraction from PowerPoint is 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 another domain, and more surprisingly we can even train models in one language and apply them to another language. Moreover, we can significantly improve search ranking results in do 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).
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.
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.
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.
6104069
Exploiting the Synergy between Peer-to-Peer and Mobile Ad Hoc Networks
We argue that there exists a synergy between peer-to-peer (p2p) overlay networks for the Internet and mobile ad hoc networks (MANETs) connecting mobile nodes communicating with each other via multi-hop wireless links-both share the key characteristics of self-organization and decentralization, and both need to solve the same fundamental problem, that is, how to provide connectivity in a decentralized, dynamic environment. We propose Dynamic P2P Source Routing (DPSR), a new routing protocol for MANETs that exploits the synergy between p2p and MANETs for increased scalability. By integrating Dynamic Source Routing (DSR) and a proximity-aware structured p2p overlay routing protocol, DPSR limits the number of the source routes that each node has to discover and rediscover to O(logN), while retaining all the attributes of DSR for dealing with the specifics of ad hoc networks. This is in contrast to the maximum of N source routes each node has to maintain in DSR. Thus DPSR has the potential to be more scalable than previous routing protocols for MANETs, such as DSR and AODV. 
 
In addition to being a network layer multi-hop routing protocol, DPSR simultaneously implements a distributed hash table (DHT) in MANETs; it implements the same functionalities as CAN, Chord, Pastry, and Tapestry, which can be exposed to the applications built on top of it via a set of common p2p APIs.
16350167
Efficient propagation for face annotation in family albums
In this paper, we propose and investigate a new user scenario for face annotation, in which users are allowed to multi-select a group of otogras and assign names to these otogras. The system will then attempt to propagate names from otogra level to face level, i.e. to infer the correspondence between name and face. Given the face similarity measure which combines methodologies from face recognition and content-based image retrieval, we formulate name propagation as an optimization problem. We define the objective function as the sum of similarities between each pair of faces of the same individual in different otogras, and propose an iterative optimization algorithm to infer the optimal correspondence. To make the propagation result reliable, a reject scheme is adopted to reject those with low confidence scores. Furthermore, we investigate the combination and alternation of browsing mode for propagation and viewer mode for annotation, so that each mode can benefit from additional inputs from the other mode. The experimental evaluation has been conducted within a typical family album of over one thousand otogras and the results show that the proposed approach is effective and efficient in automated face annotation in family albums.
8794069
"Take One Get One Free": Leveraging P2P Networks for Content Promotion
The nature of digital content is undergoing a radical transformation due to the growing infusion of user-generated content. Users that generate content have a strong motivation to actively promote their content in order to achieve publicity, recognition or to simply spread their viewpoints, knowledge and creations. Existing content sharing mechanisms available to such users, such as in P2P file sharing, are pull-based, relegating the user to a passive role. To enable active participation from users, we propose Promoter, a framework that enhances P2P file sharing systems to support content promotion in addition to the available pull-based content search. Promotion is enabled by publicizing a user's content to peers and disseminating the content by incentivizing peers to download and further propagate it.
206501510
Head pose estimation using Fisher Manifold learning
Here, we propose a new learning strategy for head pose estimation. Our approach uses nonlinear interpolation to estimate the head pose using the learning result from face images of two head poses. Advantage of our method to regression method is that it only requires training images of two head poses and better generalization ability. It outperforms existed methods, such as regression and multiclass classification method, on both synthesis and real face images. Average head pose estimation error of yaw rotation is about 4/sup 0/, which proves that our method is effective in head pose estimation.
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.