Query enrichment for web-query classification
Web-search queries are typically short and ambiguous. To classify these queries into certain target categories is a difficult but important problem. In this article, we present a new technique called query enrichment, which takes a short query and maps it to intermediate objects. Based on the collected intermediate objects, the query is then mapped to target categories. To build the necessary mapping functions, we use an ensemble of search engines to produce an enrichment of the queries. Our technique was applied to the ACM Knowledge Discovery and Data Mining competition (ACM KDDCUP) in 2005, where we won the championship on all three evaluation metrics (precision, F1 measure, which combines precision and recall, and creativity, which is judged by the organizers) among a total of 33 teams worldwide. In this article, we show that, despite the difficulty of an abundance of ambiguous queries and lack of training data, our query-enrichment technique can solve the problem satisfactorily through a two-phase classification framework. We present a detailed description of our algorithm and experimental evaluation. Our best result for F1 and precision is 42.4% and 44.4%, respectively, which is 9.6% and 24.3% higher than those from the runner-ups, respectively.

title: Latent semantic analysis for multiple-type interrelated data objects
abstract: Co-occurrence data is quite common in many real applications. Latent Semantic Analysis (LSA) has been successfully used to identify semantic relations in such data. However, LSA can only handle a single co-occurrence relationship between two types of objects. In practical applications, there are many cases where multiple types of objects exist and any pair of these objects could have a pairwise co-occurrence relation. All these co-occurrence relations can be exploited to alleviate data sparseness or to represent objects more meaningfully. In this paper, we propose a novel algorithm, M-LSA, which conducts latent semantic analysis by incorporating all pairwise co-occurrences among multiple types of objects. Based on the mutual reinforcement principle, M-LSA identifies the most salient concepts among the co-occurrence data and represents all the objects in a unified semantic space. M-LSA is general and we show that several variants of LSA are special cases of our algorithm. Experiment results show that M-LSA outperforms LSA on multiple applications, including collaborative filtering, text clustering, and text categorization.

title: Supervised latent semantic indexing for document categorization
abstract: Latent semantic indexing (LSI) is a successful technology in information retrieval (IR) which attempts to explore the latent semantics implied by a query or a document through representing them in a dimension-reduced space. However, LSI is not optimal for document categorization tasks because it aims to find the most representative features for document representation rather than the most discriminative ones. In this paper, we propose supervised LSI (SLSI) which selects the most discriminative basis vectors using the training data iteratively. The extracted vectors are then used to project the documents into a reduced dimensional space for better classification. Experimental evaluations show that the SLSI approach leads to dramatic dimension reduction while achieving good classification results.

title: CubeSVD: a novel approach to personalized Web search
abstract: As the competition of Web search market increases, there is a high demand for personalized Web search to conduct retrieval incorporating Web users' information needs. This paper focuses on utilizing clickthrough data to improve Web search. Since millions of searches are conducted everyday, a search engine accumulates a large volume of clickthrough data, which records who submits queries and which pages he/she clicks on. The clickthrough data is highly sparse and contains different types of objects (user, query and Web page), and the relationships among these objects are also very complicated. By performing analysis on these data, we attempt to discover Web users' interests and the patterns that users locate information. In this paper, a novel approach CubeSVD is proposed to improve Web search. The clickthrough data is represented by a 3-order tensor, on which we perform 3-mode analysis using the higher-order singular value decomposition technique to automatically capture the latent factors that govern the relations among these multi-type objects: users, queries and Web pages. A tensor reconstructed based on the CubeSVD analysis reflects both the observed interactions among these objects and the implicit associations among them. Therefore, Web search activities can be carried out based on CubeSVD analysis. Experimental evaluations using a real-world data set collected from an MSN search engine show that CubeSVD achieves encouraging search results in comparison with some standard methods.

title: Document Summarization Using Conditional Random Fields
abstract: Many methods, including supervised and unsupervised algorithms, have been developed for extractive document summarization. Most supervised methods consider the summarization task as a two-class classification problem and classify each sentence individually without leveraging the relationship among sentences. The unsupervised methods use heuristic rules to select the most informative sentences into a summary directly, which are hard to generalize. In this paper, we present a Conditional Random Fields (CRF) based framework to keep the merits of the above two kinds of approaches while avoiding their disadvantages. What is more, the proposed framework can take the outcomes of previous methods as features and seamlessly integrate them. The key idea of our approach is to treat the summarization task as a sequence labeling problem. In this view, each document is a sequence of sentences and the summarization procedure labels the sentences by 1 and 0. The label of a sentence depends on the assignment of labels of others. We compared our proposed approach with eight existing methods on an open benchmark data set. The results show that our approach can improve the performance by more than 7.1% and 12.1% over the best supervised baseline and unsupervised baseline respectively in terms of two popular metrics F1 and ROUGE-2. Detailed analysis of the improvement is presented as well.

title: A practical system of keyphrase extraction for web pages
abstract: Keyphrases can be used to facilitate Web users grasping the main topic(s) of a Web page. We present a practical system of automatic keyphrase extraction for Web pages. In this system, a regression model was first trained based on a set of human-labeled documents. Then it was used to extract keyphrases from new pages automatically. This paper makes three contributions. First, the structure information in a Web page was investigated for keyphrase extraction task. Second, the query log data associated with a Web page collected by a search engine server were used to help keyphrase extraction. Third, a method was put forward in this paper in order to evaluate the similarity of phrases.

title: A XML/XSL approach to visualize and animate TCOZ
abstract: The challenge for system specification is how to visually and precisely capture static, dynamic and real-time system properties in a highly structured way. Timed Communicating Object-Z (TCOZ) is an integrated formal notation that build on Object-Z's strengths in modeling complex data structures, and on Timed CSP's strengths in modeling real-time interactions. In this paper, we demonstrate approaches of using XML/XSL as a transformation tool to visualize TCOZ models into various UML diagrams and to animate TCOZ specifications with a multi-paradigm programming language-Oz.

title: Automatic cascade training with perturbation bias
abstract: Face detection methods based on cascade architecture have demonstrated fast and robust performance. Cascade learning is aided by the modularity of the architecture in which nodes are chained together to form a cascade. In this paper we present two new cascade learning results which address the decoupled nature of the cascade learning task. First, we introduce a cascade indifference curve framework, which connects the learning objectives for a node to the overall cascade performance. We derive a new cost function for node learning, which yields fully-automatic stopping conditions and improved detection performance. Second, we introduce the concept of perturbation bias, which leverages the statistical differences between target and non-target classes in a detection problem to obtain improved performance and robustness. We derive necessary and sufficient conditions for the success of the method and present experimental results.

title: Analysis of predictive spatio-temporal queries
abstract: Given a set of objects <i>S</i>, a spatio-temporal <i>window query q</i> retrieves the objects of <i>S</i> that will intersect the window during the (future) interval <i>q</i><sub><i>T</i></sub>. A <i>nearest neighbor query q</i> retrieves the objects of <i>S</i> closest to <i>q</i> during <i>q</i><sub><i>T</i></sub>. Given a threshold <i>d</i>, a spatio-temporal <i>join</i> retrieves the pairs of objects from two datasets that will come within distance <i>d</i> from each other during <i>q</i><sub><i>T</i></sub>. In this article, we present probabilistic cost models that estimate the <i>selectivity</i> of spatio-temporal window queries and joins, and the <i>expected distance</i> between a query and its nearest neighbor(s). Our models capture any query/object mobility combination (moving queries, moving objects or both) and any data type (points and rectangles) in arbitrary dimensionality. In addition, we develop specialized spatio-temporal histograms, which take into account both location and velocity information, and can be incrementally maintained. Extensive performance evaluation verifies that the proposed techniques produce highly accurate estimation on both uniform and non-uniform data.

title: A Data Mining Based Intrusion Detection Model
abstract: Intrusion Detection Systems (IDSs) have become a critical part of security systems. The goal of an intrusion detection system is to block intrusion effectively and accurately. However, the performance of IDS is not satisfying. In this paper, we study the issue of building a data mining based intrusion detection model to raise the detection performance. The key ideas are to use data mining techniques to discover consistent and useful patterns for intrusion and use the set of patterns to recognize intrusion. By applying statistics inference theory to this model, the patterns mined from a set of test data are effective to detect the attacks in the same category, and therefore can detect most novel attacks that are variants of known attacks.

title: Research into Verifying Semistructured Data
abstract: Semistructured data is now widely used in both web applications and database systems. Much of the research into this area defines algorithms that transform the data and schema, such as data integration, change management, view definition, and data normalization. While some researchers have defined a formalism for the work they have undertaken, there is no widely accepted formalism that can be used for the comparison of algorithms within these areas. The requirements of a formalism that would be helpful in these situations are that it must capture all the necessary semantics required to model the algorithms, it should not be too complex and it should be easy to use. This paper describes a first step in defining such a formalism. We have modelled the semantics expressed in the ORA-SS (Object Relationship Attribute data model for SemiStructured data) data modelling notation in two formal languages that have automatic verification tools. We compare the two models and present the findings.