36507
Ranking definitions with supervised learning methods
This paper is concerned with the problem of definition search. Specifically, given a term, we are to retrieve definitional excerpts of the term and rank the extracted excerpts according to their likelihood of being good definitions. This is in contrast to the traditional approaches of either generating a single combined definition or simply outputting all retrieved definitions. Definition ranking is essential for the task. Methods for performing definition ranking are proposed in this paper, which formalize the problem as either classification or ordinal regression. A specification for judging the goodness of a definition is given. We employ SVM as the classification model and Ranking SVM as the ordinal regression model respectively, such that they rank definition candidates according to their likelihood of being good definitions. Features for constructing the SVM and Ranking SVM models are defined. An enterprise search system based on this method has been developed and has been put into practical use. Experimental results indicate that the use of SVM and Ranking SVM can significantly outperform the baseline methods of using heuristic rules or employing the conventional information retrieval method of Okapi. This is true both when the answers are paragraphs and when 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.
6867693
Searching Documents Based on Relevance and Type
This paper extends previous work on document retrieval and document type classification, addressing the problem of 'typed search'. Specifically, given a query and a designated document type, the search system retrieves and ranks documents not only based on the relevance to the query, but also based on the likelihood of being the designated document type. The paper formalizes the problem in a general framework consisting of 'relevance model' and 'type model'. The relevance model indicates whether or not a document is relevant to a query. The type model indicates whether or not a document belongs to the designated document type. We consider three methods for combing the models: linear combination of scores, thresholding on the type score, and a hybrid of the previous two methods. We take course page search and instruction document search as examples and have conducted a series of experiments. Experimental results show our proposed approaches can significantly outperform the baseline methods.
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.
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.
19187096
Traveling salesman problem of segments
In this paper, we present a polynomial time approximation scheme (PTAS) for a variant of the traveling salesman problem (called segment TSP) in which a traveling salesman tour is sought to traverse a set of n ∊-separated segments in two dimensional space. Our results are based on an interesting combinatorial result which bounds the total number of entry points in an optimal TSP tour and a generalization of Arora's technique5 for Euclidean TSP (of a set of points). The randomized version of our algorithm takes O(n2(log n)O(1/∊2)) time to compute a (1+∊)-approximation with probability ≥l/2, and can be derandomized with an additional factor of O(n2).
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.
11432676
Building Embedded Fault-Tolerant Systems for Critical Applications: An Experimental Study
An increasing range of industries have a growing dependence on embedded software systems, many of which are safety-critical, real-time applications that require extremely high dependability. Two fundamental approaches — fault avoidance and fault tolerance — have been proposed to increase the overall dependability of such systems. However, the increased cost of using the fault tolerance approach may mean that this increase in dependability is not worth the extra expense involved. We describe an experiment undertaken in order to establish whether or not software redundancy (or the multi-version design method) can offer increased dependability over the traditional single-version development approach when given the same level of resources. The results of this and a subsequent follow-up study are then given. The analytic results from these experiments show that despite the poor quality of individual versions, the multi-version method results in a safer system than the single-version solution. It is evident that regarding the single-version method as a “seem-to-be” safer design decision for critical applications is not generally justifiable.
789303
DNA Sequence Design Based on Template Strategy
In DNA based computation and DNA nanotechnology, the design of proper DNA sequences has turned out to be an elementary problem. This paper takes a further look at the template strategy proposed in work by Frutos, A. G. et al. (Nucleic Acids Res. 1997, 25, 4748-4757). The H-measure proposed by Garzon et al. (Proceedings of the Second Annual Genetic Programming Conference, 1997; pp 472-487) is combined in this strategy to optimize the template and map sets obtained. Finally we describe a constructing method that can still produce more sequences by the results obtained in this paper.
38590214
Motion Composition of 3D Video
3D video, which is composed of a sequence of mesh models and can provide the user with interactivity, is attracting increasing attention in many research groups. However, it is time-consuming and expensive to generate 3D video sequences. In this paper, a motion composition method is proposed to edit 3D video based on the user’s requirements so that 3D video can be re-used. By analyzing the feature vectors, the hierarchical motion structure is parsed and then a motion database is set up by selecting the representative motions. A motion graph is constructed to organize the motion database by finding the possible motion transitions. Then, the best path is searched based on a proposed cost function by a modified Dijkstra algorithm after the user selects the desired motions in the motion database, which are called key motions in this paper. Our experimental results show the edited 3D video sequence looks natural and realistic.