15430467
A Supervised Learning Approach to Search of Definitions
This paper addresses the issue of search of definitions. Specifically, for a given term, we are to find out its definition candidates and rank the candidates according to their likelihood of being good definitions. This is in contrast to the traditional methods of either generating a single combined definition or outputting all retrieved definitions. Definition ranking is essential for tasks. A specification for judging the goodness of a definition is given. In the specification, a definition is categorized into one of the three levels: good definition, indifferent definition, or bad definition. Methods of performing definition ranking are also proposed in this paper, which formalize the problem as either classification or ordinal regression. We employ SVM (Support Vector Machines) as the classification model and Ranking SVM as the ordinal regression model respectively, and thus they rank definition candidates according to their likelihood of being good definitions. Features for constructing the SVM and Ranking SVM models are defined, which represent the characteristics of terms, definition candidate, and their relationship. Experimental results indicate that the use of SVM and Ranking SVM can significantly outperform the baseline methods such as heuristic rules, the conventional information retrieval—Okapi, or SVM regression. This is true when both the answers are paragraphs and they are sentences. Experimental results also show that SVM or Ranking SVM models trained in one domain can be adapted to another domain, indicating that generic models for definition ranking can be constructed.
15513083
Adapting ranking SVM to document retrieval
The paper is concerned with applying learning to rank to document retrieval. Ranking SVM is a typical method of learning to rank. We point out that there are two factors one must consider when applying Ranking SVM, in general a "learning to rank" method, to document retrieval. First, correctly ranking documents on the top of the result list is crucial for an Information Retrieval system. One must conduct training in a way that such ranked results are accurate. Second, the number of relevant documents can vary from query to query. One must avoid training a model biased toward queries with a large number of relevant documents. Previously, when existing methods that include Ranking SVM were applied to document retrieval, none of the two factors was taken into consideration. We show it is possible to make modifications in conventional Ranking SVM, so it can be better used for document retrieval. Specifically, we modify the "Hinge Loss" function in Ranking SVM to deal with the problems described above. We employ two methods to conduct optimization on the loss function: gradient descent and quadratic programming. Experimental results show that our method, referred to as Ranking SVM for IR, can outperform the conventional Ranking SVM and other existing methods for document retrieval on two datasets.
2595430
A new approach to intranet search based on information extraction
This paper is concerned with 'intranet search'. By intranet search, we mean searching for information on an intranet within an organization. We have found that search needs on an intranet can be categorized into types, through an analysis of survey results and an analysis of search log data. The types include searching for definitions, persons, experts, and homepages. Traditional information retrieval only focuses on search of relevant documents, but not on search of special types of information. We propose a new approach to intranet search in which we search for information in each of the special types, in addition to the traditional relevance search. Information extraction technologies can play key roles in such kind of 'search by type' approach, because we must first extract from the documents the necessary information in each type. We have developed an intranet search system called 'Information Desk'. In the system, we try to address the most important types of search first - finding term definitions, homepages of groups or topics, employees' personal information and experts on topics. For each type of search, we use information extraction technologies to extract, fuse, and summarize information in advance. The system is in operation on the intranet of Microsoft and receives accesses from about 500 employees per month. Feedbacks from users and system logs show that users consider the approach useful and the system can really help people to find information. This paper describes the architecture, features, component technologies, and evaluation results of the system.
1351954
Cost-Sensitive Learning of SVM for Ranking
In this paper, we propose a new method for learning to rank. ‘Ranking SVM' is a method for performing the task. It formulizes the problem as that of binary classification on instance pairs and performs the classification by means of Support Vector Machines (SVM). In Ranking SVM, the losses for incorrect classifications of instance pairs between different rank pairs are defined as the same. We note that in many applications such as information retrieval the negative effects of making errors between higher ranks and lower ranks are larger than making errors among lower ranks. Therefore, it is natural to bring in the idea of cost-sensitive learning to learning to rank, or more precisely, to set up different losses for misclassification of instance pairs between different rank pairs. Given a cost-sensitive loss function we can construct a Ranking SVM model on the basis of the loss function. Simulation results show that our method works better than Ranking SVM in practical settings of ranking. Experimental results also indicate that our method can outperform existing methods including Ranking SVM on real information retrieval tasks such as document search and definition search.
1910049
Quality is in the eye of the beholder: towards user-centric web-databases
The proliferation of database-driven web sites (or web-databases) has brought upon a plethora of applications where both Quality of Service (QoS) and Quality of Data (QoD) are of paramount importance to the end users. In our previous work, we have proposed Quality Contracts, a comprehensive framework for specifying multiple dimensions of QoS/QoD; we have also developed user-centric admission control and scheduling algorithms in web databases, whose goal is to maximize overall system performance. In this work, we turn our attention to the user side of the equation. Specifically, we propose to demonstrate how the adaptation of Quality Contracts (QCs) by the users can lead to vastly different performance results, both from the user point of view (i.e., user satisfaction) and also from the system point of view. Towards this, we propose to structure our demo in the form of an interactive game, where participants will be playing the role of users continuously adapting their QCs over time, while "playing" against system-generated users, who follow predetermined QC adaptation policies. Finally, we also propose to illustrate the effect of different admission control and scheduling policies.
17405074
Extracting Meaningful Entities from Police Narrative Reports
Valuable criminal-justice data in free texts such as police narrative reports are currently difficult to be accessed and used by intelligence investigators in crime analyses. It would be desirable to automatically identify from text reports meaningful entities, such as person names, addresses, narcotic drugs, or vehicle names to facilitate crime investigation. In this paper, we report our work on a neural network-based entity extractor, which applies named-entity extraction techniques to identify useful entities from police narrative reports. Preliminary evaluation results demonstrated that our approach is feasible and has some potential values for real-life applications. Our system achieved encouraging precision and recall rates for person names and narcotic drugs, but did not perform well for addresses and personal properties. Our future work includes conducting larger-scale evaluation studies and enhancing the system to capture human knowledge interactively.
17726179
Establishing a software architecting environment
We present the work of establishing an integrated environment that facilitates architecture design, reconstruction, and maintenance in the entire life cycle of a software product line. This architecting environment (ART environment) has been used in modeling and analysis of both the designed platform architecture model and the reverse-engineered product implementation architecture models of different releases in a big product line of Nokia mobile terminals. ART environment comprises tools for architecture model validation, architecture model analysis and processing, and reverse architecting. The ART environment fits the current software development process inside Nokia, and is integrated with the design and documentation tools that have already been used by Nokia software architects. UML, after being customized with UML profiles for architecture design, is used as the architecture modeling language in ART environment.
6701763
UML-based reverse engineering and model analysis approaches for software architecture maintenance
This paper proposes a UML-based software maintenance process. The process is guided by architectural descriptions and existing architectural models. The descriptions are given as variants of UML profiles describing the styles and rules relevant for a particular application domain. A reverse engineering subprocess, combining top-down and bottom-up reverse engineering activities, aims at constructing the architectural models. Resulting models are investigated in a model analysis subprocess. The models are checked against the profiles to find violations against the given architectural rules when maintaining and developing the subject system, and they are further analyzed using a set of UML model processing operations. The proposed approach is applied for maintaining a large-scale product platform architecture and real-life product-line products built on top of this platform. The model analysis results of the case study are discussed.
19232033
Protein Threading by Linear Programming: Theoretical Analysis and Computational Results
In a previous paper (Xu, Li, Kim, and Xu, Journal of Bioinformatics and Computational Biology, vol. 1, no. 1, pp. 95–117, 2003), we have used an integer programming approach to implement a protein threading program RAPTOR for protein 3D structure prediction, based on the threading model treating pairwise contacts rigorously and allowing variable gaps. We have solved the integer program by the canonical branch-and-bound method. In this paper we present a branch-and-cut method, a careful theoretical analysis of our formulation and why our approach is so effective. The result of cutting plane analysis is that two types of well-known cuts for this problem are already implied in the constraint set, which provides us some intuition that our formulation would be very effective. Experimental results show that for about 99 percent of real threading instances, the linear relaxations of their integer programs solve to integral optimal solutions directly. For the rest one percent of real instances, the integral solutions can be obtained with only several branch nodes. Experimental results also show that no special template or sequence features result in more possibilities of fractional solutions. This indicates that extra effort to seek for cutting planes to strengthen the existing formulation is unnecessary.