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Diverse and proportional size-l object summaries using pairwise relevance

VLDB JOURNAL 2016年第6期25卷 791-816页

作者： Fakas, Georgios J. Cai, Zhi Mamoulis, Nikos Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Hong Kong Peoples R China Beijing Univ Technol Coll Comp Sci Beijing Peoples R China Univ Hong Kong Dept Comp Sci Pokfulam Hong Kong Peoples R China

The abundance and ubiquity of graphs (e.g., online social networks such as Google and Facebook;bibliographic graphs such as DBLP) necessitates the effective and efficient search over them. Given a set of keywords that can identify a data subject (DS), a recently proposed keyword search paradigm produces a set of object summaries (OSs) as results. An OS is a tree structure rooted at the DS node (i.e., a node containing the keywords) with surrounding nodes that summarize all data held on the graph about the DS. OS snippets, denoted as size-l OSs, have also been investigated. A size-l OS is a partial OS containing l nodes such that the summation of their importance scores results in the maximum possible total score. However, the set of nodes that maximize the total importance score may result in an uninformative size-l OSs, as very important nodes may be repeated in it, dominating other representative information. In view of this limitation, in this paper, we investigate the effective and efficient generation of two novel types of OS snippets, i.e., diverse and proportional size-l OSs, denoted as DSize-l and PSize-l OSs. Namely, besides the importance of each node, we also consider its pairwise relevance (similarity) to the other nodes in the OS and the snippet. We conduct an extensive evaluation on two real graphs (DBLP and Google). We verify effectiveness by collecting user feedback, e.g., by asking DBLP authors (i.e., the DSs themselves) to evaluate our results. In addition, we verify the efficiency of our algorithms and evaluate the quality of the snippets that they produce.

关键词： Keyword search Diversity Proportionality snippets Summaries

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Snippet counting for cotton length distribution measurement using image analysis

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TEXTILE RESEARCH JOURNAL 2008年第4期78卷 336-341页

作者： Xu, Weilin Xu, Bugao Li, Wenbin Cui, Weigang Wuhan Univ Sci & Engn Wuhan 430073 Peoples R China Univ Texas Austin Dept Human Ecol Austin TX 78712 USA

This paper presents a new method of measuring the number-length distribution of cotton fibers using a snippet-counting method and image analysis techniques. The test samples were aligned at the baseline and combed to form a tuft web, or beard, which was cut into a number of segments of a specific length by a fiber cutter. The snippets in different cut segments were collected and distributed on separate black glossy papers, and then they were digitized and scanned in order into a computer for image analysis. The scanning order indicated the distance of each segment from the cutting baseline, and the number of the snippets on a glossy paper was the frequency of fibers present in the current segment. A specially designed image analysis program was used to trace all snippets in a scanned image, so that the number of snippets could be calculated. After all of the images were analyzed, the fiber numbers in different segments could be summarized, and from these number-length data the distribution, maximum length, mean length, and other fiber statistics could be derived.

关键词： cotton fiber image analysis number-length distribution snippets

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Query Expansion Using Proposed Location-Based Algorithm for Hindi-English CLIR: Analyzing Three Test Collections

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2024年第5期38卷 2459001-2459001页

作者： Chandra, Ganesh Dwivedi, Sanjay K. Madhav Inst Sci & Technol Dept Comp Sci & Engn Gwalior Madhya Pradesh India Babasaheb Bhimrao Ambedkar A Cent Univ Dept Comp Sci Lucknow Uttar Pradesh India

The rapid growth of contents on the Web in different languages increases the demand of Cross-Lingual Information Retrieval (CLIR). The accuracy of result suffers due to many problems such as ambiguity and drift issue in query. Query Expansion (QE) offers reliable solution for obtaining suitable documents for user queries. In this paper, we proposed an architecture for Hindi-English CLIR system using QE for improving the relevancy of retrieved results. In this architecture, for the addition of term(s) at appropriate position(s), we proposed a location-based algorithm to resolve the drift query issue in QE. User queries in Hindi language have been translated into document language (i.e. English) and the accuracy of translation is improved using Back-Translation. Google search has been performed and the retrieved documents are ranked using Okapi BM25 to arrange the documents in the order of decreasing relevancy to select the most suitable terms for QE. We used term selection value (TSV) for QE and for retrieving the terms, we created three test collections namely the (i) description and narration of the Forum for Information Retrieval Evaluation (FIRE) dataset, (ii) snippets of retrieved documents against each query and (iii) Nearest-Neighborhood (NN) words against each query word among the ranked documents. To evaluate the system, 50 queries of Hindi language are selected from the FIRE-2012 dataset. In this paper, we performed two experiments: (i) impact of the proposed location-based algorithm on the proposed architecture of CLIR;and (ii) analysis of QE using three datasets, i.e. FIRE, NN and snippets. In the first case, result shows that the relevancy of Hindi-English CLIR is improved by performing QE using the location-based algorithm and a 12% of improvement is achieved as compared to the results of QE obtained without applying the location-based algorithm. In the second case, the location-based algorithm is applied on three datasets. The Mean Average Precision (M

关键词： CLIR FIRE snippets Nearest-Neighborhood Query Expansion

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Reproducibility of issues reported in stack overflow questions: Challenges, impact & estimation

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JOURNAL OF SYSTEMS AND SOFTWARE 2024年 217卷

作者： Mondal, Saikat Roy, Banani Univ Saskatchewan Dept Comp Sci Saskatoon SK Canada

Software developers often submit questions to technical Q&A sites like Stack Overflow (SO) to resolve codelevel problems. In practice, they include example code snippets with questions to explain the programming issues. Existing research suggests that users attempt to reproduce the reported issues using given code snippets when answering questions. Unfortunately, such code snippets could not always reproduce the issues due to several unmet challenges that prevent questions from receiving appropriate and prompt solutions. One previous study investigated reproducibility challenges discussed in 400 Java questions and produced a catalog of them (e.g., too short code snippets). However, it is unknown how the practitioners (i.e., SO users) perceive this challenge catalog. Practitioners' perspectives are inevitable in validating these challenges and estimating their severity. In this study, we first surveyed 53 practitioners to understand their perspectives on reproducibility challenges. We attempt to (a) see whether they agree with these challenges, (b) determine the impact of each challenge on answering questions, and (c) identify the need for tools to promote reproducibility. Survey results show that - (a) about 90% of the participants agree with the challenges, (b) "missing an important part of code"most severely hurt reproducibility, and (c) participants strongly recommend introducing automated tool support to promote reproducibility. Second, we extract nine code-based features (e.g., LOC, compilability) and build five Machine Learning (ML) models to predict issue reproducibility. Early detection might help users improve code snippets and their reproducibility. Our models achieve 84.5% precision, 83.0% recall, 82.8% F1-score, and 82.8% overall accuracy, which are highly promising. We also validate the effectiveness of our features by predicting the reproducibility status of C# code snippets. Third, we systematically interpret the ML model and explain how code snippets

关键词： Stack Overflow Issue reproducibility Code snippets Reproducibility challenges User study

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Efficient Index-Based Snippet Generation

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ACM TRANSACTIONS ON INFORMATION SYSTEMS 2014年第2期32卷 1–24页

作者： Bast, Hannah Celikik, Marjan Univ Freiburg Dept Comp Sci Freiburg Germany

Ranked result lists with query-dependent snippets have become state of the art in text search. They are typically implemented by searching, at query time, for occurrences of the query words in the top-ranked documents. This document-based approach has three inherent problems: (i) when a document is indexed by terms which it does not contain literally (e.g., related words or spelling variants), localization of the corresponding snippets becomes problematic;(ii) each query operator (e.g., phrase or proximity search) has to be implemented twice, on the index side in order to compute the correct result set, and on the snippet-generation side to generate the appropriate snippets;and (iii) in a worst case, the whole document needs to be scanned for occurrences of the query words, which could be problematic for very long documents. We present a new index-based method that localizes snippets by information solely computed from the index and that overcomes all three problems. Unlike previous index-based methods, we show how to achieve this at essentially no extra cost in query processing time, by a technique we call operator inversion. We also show how our index-based method allows the caching of individual segments instead of complete documents, which enables a significantly larger cache hit-ratio as compared to the document-based approach. We have fully integrated our implementation with the CompleteSearch engine.

关键词： Algorithms Design Experimentation Performance Theory snippets document summarization advanced search caching efficiency

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ReClose: web page summarization combining summary techniques

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INTERNATIONAL JOURNAL OF WEB INFORMATION SYSTEMS 2011年第4期7卷 333-359页

作者： Wenerstrom, Brent Kantardzic, Mehmed Univ Louisville Dept Comp Engn & Comp Sci Louisville KY 40292 USA

Purpose - Search engine users are faced with long lists of search results, each entry being of a varying degree of relevance. Often users' expectations based on the short text of a search result hold false expectations about the linked web page. This leads users to skip relevant information, missing valuable insights, and click on irrelevant web pages wasting time. The purpose of this paper is to propose a new summary generation technique, ReClose, which combines query-independent and query-biased summary techniques to improve the accuracy of users' expectations. Design/methodology/approach - The authors tested the effectiveness of ReClose summaries against Google summaries by surveying 34 participants. Participants were randomly assigned to use one type of summary approach. Summary effectiveness was judged based on the accuracy of each user's expectations. Findings - It was found that individuals using ReClose summaries showed a 10 per cent increase in the expectation accuracy over individuals using Google summaries, and therefore better user satisfaction. Practical implications - The survey demonstrates the effectiveness of using ReClose summaries to improve the accuracy of user expectations. Originality/value - This paper presents a novel summary generation technique called ReClose, a new approach to summary evaluation and improvements upon previously proposed summary generation techniques.

关键词： Internet Search engines Information management Web sites Automatic summarization snippets Web page summaries

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Optimizing word search within documents by showing results in the context

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JOURNAL OF VISUALIZATION 2023年第2期26卷 441-455页

作者： Peng, Cheng Ge, Tong Wang, Yunhai Shandong Univ Sch Comp Sci & Technol Aoshanwei St Qingdao 250100 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Taoyuan St Shenzhen 518055 Peoples R China

Searching for text in a document is a day-to-day task supported by a variety of visual interface techniques. Since the target searching word may be scattered across the document, manually checking each word is exhaustive and time-consuming, which calls for effective visualization of the search results to enable efficient visual document analysis. In this paper, we revisit the problem of visualizing word search results and formulate two design goals: minimizing the number of user interactions and optimizing the onscreen information of search results. Two visualization techniques are introduced accordingly: a structured list that integrates search results with the hierarchical document structure and full sentence snippets to provide more complete local context for each matched result. We then conduct a user study to compare these techniques with some most commonly used visual interfaces. The results show that the structured list is preferred by most participants and leads to more efficient search, whereas the full sentence snippet results in a slight improvement in accuracy and efficiency, but is still appreciated by the participants.

关键词： Word search Text visualization User interfaces snippets User study

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Automatic classification of web search results: Product review vs. non-review documents

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10th International Conference on Asian Digital Libraries

作者： Thet, Tun Thura Na, Jin-Cheon Khoo, Christopher S. G. Nanyang Technol Univ Wim Kee Wee Sch Commun & Informat Singapore 637718 Singapore

ISBN: (纸本)9783540770930

This study seeks to develop an automatic method to identify product review documents on the Web using the snippets (summary information that includes the URL, title, and summary text) returned by the Web search engine. The aim is to allow the user to extend topical search with genre-based filtering or categorization. Firstly we applied a common machine learning technique, SVM (Support Vector Machine), to investigate which features of the snippets are useful for classification. The best results were obtained using just the title and URL (domain and folder names) of the snippets as phrase terms (n-grams). Then we developed a heuristic approach that utilizes domain knowledge constructed semi-automatically, and found that it performs comparatively well, with only a small drop in accuracy rates. A hybrid approach which combines both the machine learning and heuristic approaches performs slightly better than the machine learning approach alone.

关键词： product review documents genre classification snippets web search results

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Conditional distribution regression for functional responses

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SCANDINAVIAN JOURNAL OF STATISTICS 2022年第2期49卷 502-524页

作者： Fan, Jianing Muller, Hans-Georg Univ Calif Davis Dept Stat Davis CA 95616 USA

Modeling conditional distributions for functional data extends the concept of a mean response in functional regression settings, where vector predictors are paired with functional responses. This extension is challenging because of the nonexistence of well-defined densities, cumulative distributions, or quantile functions in the Hilbert space where the response functions are located. To address this challenge, we simplify the problem by assuming that the response functions are Gaussian processes, which means that the conditional distribution of the responses is determined by conditional mean and conditional covariance. We demonstrate that these quantities can be obtained by applying global and local Frechet regression, where the local version is more flexible and applicable when the covariate dimension is low and covariates are continuous, while the global version is not subject to these restrictions but is based on the assumption of a more restrictive regression relation. Convergence rates for the proposed estimates are obtained under the framework of M-estimation. The corresponding estimation of conditional distributions is illustrated with simulations and an application to bike-sharing data, where predictors include weather characteristics and responses are bike rental profiles. We also show that our methods are applicable to the challenging problem to study functional fragments. Such data are observed in accelerated longitudinal studies and correspond to functional data observed over short domain segments. We demonstrate the utility of conditional distributions in this context by using the time (age) at which a subject enters the domain of a fragment in addition to other covariates as predictor and the function observed over the domain of the fragment as response.

关键词： conditional covariance conditional mean Frechet regression fragments functional data analysis functional regression Gaussian process prediction band snippets Wasserstein metric

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Generating Comparative Summaries from Reviews 13

Generating Comparative Summaries from Reviews

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22nd ACM International Conference on Information and Knowledge Management (CIKM)

作者： Sipos, Ruben Joachims, Thorsten Cornell Univ Dept Comp Sci Ithaca NY 14853 USA

ISBN: (纸本)9781450322638

To facilitate direct comparisons between different products, we present an approach to constructing short and comparative summaries based on product reviews. In particular, the user can view automatically aligned pairs of snippets describing reviewers' opinions on different features (also selected automatically by our approach) for two selected products. We propose a submodular objective function that avoids redundancy, that is efficient to optimize, and that aligns the snippets into pairs. snippets are chosen from product reviews and thus easy to obtain. In our experiments, we show that the method constructs qualitatively good summaries, and that it can be tuned via supervised learning.

关键词： summarization reviews comparison snippets pairs submodular

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