Search (126 results, page 1 of 7)

  • × theme_ss:"Data Mining"
  1. Baeza-Yates, R.; Hurtado, C.; Mendoza, M.: Improving search engines by query clustering (2007) 0.03 
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    Abstract
    In this paper, we present a framework for clustering Web search engine queries whose aim is to identify groups of queries used to search for similar information on the Web. The framework is based on a novel term vector model of queries that integrates user selections and the content of selected documents extracted from the logs of a search engine. The query representation obtained allows us to treat query clustering similarly to standard document clustering. We study the application of the clustering framework to two problems: relevance ranking boosting and query recommendation. Finally, we evaluate with experiments the effectiveness of our approach.
    Footnote
    Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"
    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1793-1804
  2. Lihui, C.; Lian, C.W.: Using Web structure and summarisation techniques for Web content mining (2005) 0.02 
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    Abstract
    The dynamic nature and size of the Internet can result in difficulty finding relevant information. Most users typically express their information need via short queries to search engines and they often have to physically sift through the search results based on relevance ranking set by the search engines, making the process of relevance judgement time-consuming. In this paper, we describe a novel representation technique which makes use of the Web structure together with summarisation techniques to better represent knowledge in actual Web Documents. We named the proposed technique as Semantic Virtual Document (SVD). We will discuss how the proposed SVD can be used together with a suitable clustering algorithm to achieve an automatic content-based categorization of similar Web Documents. The auto-categorization facility as well as a "Tree-like" Graphical User Interface (GUI) for post-retrieval document browsing enhances the relevance judgement process for Internet users. Furthermore, we will introduce how our cluster-biased automatic query expansion technique can be used to overcome the ambiguity of short queries typically given by users. We will outline our experimental design to evaluate the effectiveness of the proposed SVD for representation and present a prototype called iSEARCH (Intelligent SEarch And Review of Cluster Hierarchy) for Web content mining. Our results confirm, quantify and extend previous research using Web structure and summarisation techniques, introducing novel techniques for knowledge representation to enhance Web content mining.
    Source
    Information processing and management. 41(2005) no.5, S.1225-1242
  3. Ku, L.-W.; Chen, H.-H.: Mining opinions from the Web : beyond relevance retrieval (2007) 0.02 
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    Abstract
    Documents discussing public affairs, common themes, interesting products, and so on, are reported and distributed on the Web. Positive and negative opinions embedded in documents are useful references and feedbacks for governments to improve their services, for companies to market their products, and for customers to purchase their objects. Web opinion mining aims to extract, summarize, and track various aspects of subjective information on the Web. Mining subjective information enables traditional information retrieval (IR) systems to retrieve more data from human viewpoints and provide information with finer granularity. Opinion extraction identifies opinion holders, extracts the relevant opinion sentences, and decides their polarities. Opinion summarization recognizes the major events embedded in documents and summarizes the supportive and the nonsupportive evidence. Opinion tracking captures subjective information from various genres and monitors the developments of opinions from spatial and temporal dimensions. To demonstrate and evaluate the proposed opinion mining algorithms, news and bloggers' articles are adopted. Documents in the evaluation corpora are tagged in different granularities from words, sentences to documents. In the experiments, positive and negative sentiment words and their weights are mined on the basis of Chinese word structures. The f-measure is 73.18% and 63.75% for verbs and nouns, respectively. Utilizing the sentiment words mined together with topical words, we achieve f-measure 62.16% at the sentence level and 74.37% at the document level.
    Footnote
    Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"
    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1838-1850
  4. Bella, A. La; Fronzetti Colladon, A.; Battistoni, E.; Castellan, S.; Francucci, M.: Assessing perceived organizational leadership styles through twitter text mining (2018) 0.01 
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    Abstract
    We propose a text classification tool based on support vector machines for the assessment of organizational leadership styles, as appearing to Twitter users. We collected Twitter data over 51 days, related to the first 30 Italian organizations in the 2015 ranking of Forbes Global 2000-out of which we selected the five with the most relevant volumes of tweets. We analyzed the communication of the company leaders, together with the dialogue among the stakeholders of each company, to understand the association with perceived leadership styles and dimensions. To assess leadership profiles, we referred to the 10-factor model developed by Barchiesi and La Bella in 2007. We maintain the distinctiveness of the approach we propose, as it allows a rapid assessment of the perceived leadership capabilities of an enterprise, as they emerge from its social media interactions. It can also be used to show how companies respond and manage their communication when specific events take place, and to assess their stakeholder's reactions.
    Source
    Journal of the Association for Information Science and Technology. 69(2018) no.1, S.21-31
  5. Goldberg, D.M.; Zaman, N.; Brahma, A.; Aloiso, M.: Are mortgage loan closing delay risks predictable? : A predictive analysis using text mining on discussion threads (2022) 0.01 
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    Abstract
    Loan processors and underwriters at mortgage firms seek to gather substantial supporting documentation to properly understand and model loan risks. In doing so, loan originations become prone to closing delays, risking client dissatisfaction and consequent revenue losses. We collaborate with a large national mortgage firm to examine the extent to which these delays are predictable, using internal discussion threads to prioritize interventions for loans most at risk. Substantial work experience is required to predict delays, and we find that even highly trained employees have difficulty predicting delays by reviewing discussion threads. We develop an array of methods to predict loan delays. We apply four modern out-of-the-box sentiment analysis techniques, two dictionary-based and two rule-based, to predict delays. We contrast these approaches with domain-specific approaches, including firm-provided keyword searches and "smoke terms" derived using machine learning. Performance varies widely across sentiment approaches; while some sentiment approaches prioritize the top-ranking records well, performance quickly declines thereafter. The firm-provided keyword searches perform at the rate of random chance. We observe that the domain-specific smoke term approaches consistently outperform other approaches and offer better prediction than loan and borrower characteristics. We conclude that text mining solutions would greatly assist mortgage firms in delay prevention.
    Source
    Journal of the Association for Information Science and Technology. 73(2022) no.3, S.419-437
  6. Liu, Y.; Zhang, M.; Cen, R.; Ru, L.; Ma, S.: Data cleansing for Web information retrieval using query independent features (2007) 0.01 
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    Abstract
    Understanding what kinds of Web pages are the most useful for Web search engine users is a critical task in Web information retrieval (IR). Most previous works used hyperlink analysis algorithms to solve this problem. However, little research has been focused on query-independent Web data cleansing for Web IR. In this paper, we first provide analysis of the differences between retrieval target pages and ordinary ones based on more than 30 million Web pages obtained from both the Text Retrieval Conference (TREC) and a widely used Chinese search engine, SOGOU (www.sogou.com). We further propose a learning-based data cleansing algorithm for reducing Web pages that are unlikely to be useful for user requests. We found that there exists a large proportion of low-quality Web pages in both the English and the Chinese Web page corpus, and retrieval target pages can be identified using query-independent features and cleansing algorithms. The experimental results showed that our algorithm is effective in reducing a large portion of Web pages with a small loss in retrieval target pages. It makes it possible for Web IR tools to meet a large fraction of users' needs with only a small part of pages on the Web. These results may help Web search engines make better use of their limited storage and computation resources to improve search performance.
    Footnote
    Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"
    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1884-1898
  7. Lam, W.; Yang, C.C.; Menczer, F.: Introduction to the special topic section on mining Web resources for enhancing information retrieval (2007) 0.01 
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    Abstract
    The amount of information on the Web has been expanding at an enormous pace. There are a variety of Web documents in different genres, such as news, reports, reviews. Traditionally, the information displayed on Web sites has been static. Recently, there are many Web sites offering content that is dynamically generated and frequently updated. It is also common for Web sites to contain information in different languages since many countries adopt more than one language. Moreover, content may exist in multimedia formats including text, images, video, and audio.
    Footnote
    Einführung in einen Themenschwerpunkt "Mining Web resources for enhancing information retrieval"
    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1791-1792
  8. Ayadi, H.; Torjmen-Khemakhem, M.; Daoud, M.; Huang, J.X.; Jemaa, M.B.: Mining correlations between medically dependent features and image retrieval models for query classification (2017) 0.01 
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    Abstract
    The abundance of medical resources has encouraged the development of systems that allow for efficient searches of information in large medical image data sets. State-of-the-art image retrieval models are classified into three categories: content-based (visual) models, textual models, and combined models. Content-based models use visual features to answer image queries, textual image retrieval models use word matching to answer textual queries, and combined image retrieval models, use both textual and visual features to answer queries. Nevertheless, most of previous works in this field have used the same image retrieval model independently of the query type. In this article, we define a list of generic and specific medical query features and exploit them in an association rule mining technique to discover correlations between query features and image retrieval models. Based on these rules, we propose to use an associative classifier (NaiveClass) to find the best suitable retrieval model given a new textual query. We also propose a second associative classifier (SmartClass) to select the most appropriate default class for the query. Experiments are performed on Medical ImageCLEF queries from 2008 to 2012 to evaluate the impact of the proposed query features on the classification performance. The results show that combining our proposed specific and generic query features is effective in query classification.
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.5, S.1323-1334
  9. Chowdhury, G.G.: Template mining for information extraction from digital documents (1999) 0.01 
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    Date
    2. 4.2000 18:01:22
  10. Sánchez, D.; Chamorro-Martínez, J.; Vila, M.A.: Modelling subjectivity in visual perception of orientation for image retrieval (2003) 0.01 
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    Abstract
    In this paper we combine computer vision and data mining techniques to model high-level concepts for image retrieval, on the basis of basic perceptual features of the human visual system. High-level concepts related to these features are learned and represented by means of a set of fuzzy association rules. The concepts so acquired can be used for image retrieval with the advantage that it is not needed to provide an image as a query. Instead, a query is formulated by using the labels that identify the learned concepts as search terms, and the retrieval process calculates the relevance of an image to the query by an inference mechanism. An additional feature of our methodology is that it can capture user's subjectivity. For that purpose, fuzzy sets theory is employed to measure user's assessments about the fulfillment of a concept by an image.
    Source
    Information processing and management. 39(2003) no.2, S.251-266
  11. Sarnikar, S.; Zhang, Z.; Zhao, J.L.: Query-performance prediction for effective query routing in domain-specific repositories (2014) 0.01 
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    Abstract
    The effective use of corporate memory is becoming increasingly important because every aspect of e-business requires access to information repositories. Unfortunately, less-than-satisfying effectiveness in state-of-the-art information-retrieval techniques is well known, even for some of the best search engines such as Google. In this study, the authors resolve this retrieval ineffectiveness problem by developing a new framework for predicting query performance, which is the first step toward better retrieval effectiveness. Specifically, they examine the relationship between query performance and query context. A query context consists of the query itself, the document collection, and the interaction between the two. The authors first analyze the characteristics of query context and develop various features for predicting query performance. Then, they propose a context-sensitive model for predicting query performance based on the characteristics of the query and the document collection. Finally, they validate this model with respect to five real-world collections of documents and demonstrate its utility in routing queries to the correct repository with high accuracy.
    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.8, S.1597-1614
  12. Saz, J.T.: Perspectivas en recuperacion y explotacion de informacion electronica : el 'data mining' (1997) 0.01 
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    Footnote
    Übers. des Titels: Perspectives on the retrieval and exploitation of electronic information: data mining
  13. Knowledge management in fuzzy databases (2000) 0.01 
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    Abstract
    The volume presents recent developments in the introduction of fuzzy, probabilistic and rough elements into basic components of fuzzy databases, and their use (notably querying and information retrieval), from the point of view of data mining and knowledge discovery. The main novel aspect of the volume is that issues related to the use of fuzzy elements in databases, database querying, information retrieval, etc. are presented and discussed from the point of view, and for the purpose of data mining and knowledge discovery that are 'hot topics' in recent years
  14. Gaizauskas, R.; Wilks, Y.: Information extraction : beyond document retrieval (1998) 0.01 
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    Abstract
    In this paper we give a synoptic view of the growth of the text processing technology of informatione xtraction (IE) whose function is to extract information about a pre-specified set of entities, relations or events from natural language texts and to record this information in structured representations called templates. Here we describe the nature of the IE task, review the history of the area from its origins in AI work in the 1960s and 70s till the present, discuss the techniques being used to carry out the task, describe application areas where IE systems are or are about to be at work, and conclude with a discussion of the challenges facing the area. What emerges is a picture of an exciting new text processing technology with a host of new applications, both on its own and in conjunction with other technologies, such as information retrieval, machine translation and data mining
  15. Survey of text mining : clustering, classification, and retrieval (2004) 0.01 
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    Abstract
    Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory. As the volume of digitized textual media continues to grow, so does the need for designing robust, scalable indexing and search strategies (software) to meet a variety of user needs. Knowledge extraction or creation from text requires systematic yet reliable processing that can be codified and adapted for changing needs and environments. This book will draw upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. It will address document identification, clustering and categorizing documents, cleaning text, and visualizing semantic models of text.
    LCSH
    Data mining ; Information retrieval
    Subject
    Data mining ; Information retrieval
  16. Biskri, I.; Rompré, L.: Using association rules for query reformulation (2012) 0.01 
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    Abstract
    In this paper the authors will present research on the combination of two methods of data mining: text classification and maximal association rules. Text classification has been the focus of interest of many researchers for a long time. However, the results take the form of lists of words (classes) that people often do not know what to do with. The use of maximal association rules induced a number of advantages: (1) the detection of dependencies and correlations between the relevant units of information (words) of different classes, (2) the extraction of hidden knowledge, often relevant, from a large volume of data. The authors will show how this combination can improve the process of information retrieval.
    Source
    Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a
  17. Berry, M.W.; Esau, R.; Kiefer, B.: ¬The use of text mining techniques in electronic discovery for legal matters (2012) 0.01 
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    Abstract
    Electronic discovery (eDiscovery) is the process of collecting and analyzing electronic documents to determine their relevance to a legal matter. Office technology has advanced and eased the requirements necessary to create a document. As such, the volume of data has outgrown the manual processes previously used to make relevance judgments. Methods of text mining and information retrieval have been put to use in eDiscovery to help tame the volume of data; however, the results have been uneven. This chapter looks at the historical bias of the collection process. The authors examine how tools like classifiers, latent semantic analysis, and non-negative matrix factorization deal with nuances of the collection process.
    Source
    Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a
  18. Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997) 0.01 
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    Abstract
    Defines digital libraries and discusses the effects of new technology on librarians. Examines the different viewpoints of librarians and information technologists on digital libraries. Describes the development of a digital library at the National Drug Intelligence Center, USA, which was carried out in collaboration with information technology experts. The system is based on Web enabled search technology to find information, data visualization and data mining to visualize it and use of SGML as an information standard to store it
    Date
    22.11.1998 18:57:22
  19. Medien-Informationsmanagement : Archivarische, dokumentarische, betriebswirtschaftliche, rechtliche und Berufsbild-Aspekte ; [Frühjahrstagung der Fachgruppe 7 im Jahr 2000 in Weimar und Folgetagung 2001 in Köln] (2003) 0.01 
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    Abstract
    Als in den siebziger Jahren des vergangenen Jahrhunderts immer häufiger die Bezeichnung Informationsmanager für Leute propagiert wurde, die bis dahin als Dokumentare firmierten, wurde dies in den etablierten Kreisen der Archivare und Bibliothekare gelegentlich belächelt und als Zeichen einer Identitätskrise oder jedenfalls einer Verunsicherung des damit überschriebenen Berufsbilds gewertet. Für den Berufsstand der Medienarchivare/Mediendokumentare, die sich seit 1960 in der Fachgruppe 7 des Vereins, später Verbands deutscher Archivare (VdA) organisieren, gehörte diese Verortung im Zeichen neuer inhaltlicher Herausforderungen (Informationsflut) und Technologien (EDV) allerdings schon früh zu den Selbstverständlichkeiten des Berufsalltags. "Halt, ohne uns geht es nicht!" lautete die Überschrift eines Artikels im Verbandsorgan "Info 7", der sich mit der Einrichtung von immer mächtigeren Leitungsnetzen und immer schnelleren Datenautobahnen beschäftigte. Information, Informationsgesellschaft: diese Begriffe wurden damals fast nur im technischen Sinne verstanden. Die informatisierte, nicht die informierte Gesellschaft stand im Vordergrund - was wiederum Kritiker auf den Plan rief, von Joseph Weizenbaum in den USA bis hin zu den Informations-Ökologen in Bremen. Bei den nationalen, manchmal auch nur regionalen Projekten und Modellversuchen mit Datenautobahnen - auch beim frühen Btx - war nie so recht deutlich geworden, welche Inhalte in welcher Gestalt durch diese Netze und Straßen gejagt werden sollten und wer diese Inhalte eigentlich selektieren, portionieren, positionieren, kurz: managen sollte. Spätestens mit dem World Wide Web sind diese Projekte denn auch obsolet geworden, jedenfalls was die Hardware und Software anging. Geblieben ist das Thema Inhalte (neudeutsch: Content). Und - immer drängender im nicht nur technischen Verständnis - das Thema Informationsmanagement. MedienInformationsManagement war die Frühjahrstagung der Fachgruppe 7 im Jahr 2000 in Weimar überschrieben, und auch die Folgetagung 2001 in Köln, die der multimedialen Produktion einen dokumentarischen Pragmatismus gegenüber stellte, handelte vom Geschäftsfeld Content und von Content-Management-Systemen. Die in diesem 6. Band der Reihe Beiträge zur Mediendokumentation versammelten Vorträge und Diskussionsbeiträge auf diesen beiden Tagungen beleuchten das Titel-Thema aus den verschiedensten Blickwinkeln: archivarischen, dokumentarischen, kaufmännischen, berufsständischen und juristischen. Deutlich wird dabei, daß die Berufsbezeichnung Medienarchivarln/Mediendokumentarln ziemlich genau für all das steht, was heute mit sog. alten wie neuen Medien im organisatorischen, d.h. ordnenden und vermittelnden Sinne geschieht. Im besonderen Maße trifft dies auf das Internet und die aus ihm geborenen Intranets zu. Beide bedürfen genauso der ordnenden Hand, die sich an den alten Medien, an Buch, Zeitung, Tonträger, Film etc. geschult hat, denn sie leben zu großen Teilen davon. Daß das Internet gleichwohl ein Medium sui generis ist und die alten Informationsberufe vor ganz neue Herausforderungen stellt - auch das durchzieht die Beiträge von Weimar und Köln.
    Content
    Enthält u.a. die Beiträge (Dokumentarische Aspekte): Günter Perers/Volker Gaese: Das DocCat-System in der Textdokumentation von Gr+J (Weimar 2000) Thomas Gerick: Finden statt suchen. Knowledge Retrieval in Wissensbanken. Mit organisiertem Wissen zu mehr Erfolg (Weimar 2000) Winfried Gödert: Aufbereitung und Rezeption von Information (Weimar 2000) Elisabeth Damen: Klassifikation als Ordnungssystem im elektronischen Pressearchiv (Köln 2001) Clemens Schlenkrich: Aspekte neuer Regelwerksarbeit - Multimediales Datenmodell für ARD und ZDF (Köln 2001) Josef Wandeler: Comprenez-vous only Bahnhof'? - Mehrsprachigkeit in der Mediendokumentation (Köln 200 1)
    Date
    11. 5.2008 19:49:22
    LCSH
    Information technology / Management / Congresses
    Subject
    Information technology / Management / Congresses
  20. Chen, Y.-L.; Liu, Y.-H.; Ho, W.-L.: ¬A text mining approach to assist the general public in the retrieval of legal documents (2013) 0.01 
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    Abstract
    Applying text mining techniques to legal issues has been an emerging research topic in recent years. Although some previous studies focused on assisting professionals in the retrieval of related legal documents, they did not take into account the general public and their difficulty in describing legal problems in professional legal terms. Because this problem has not been addressed by previous research, this study aims to design a text-mining-based method that allows the general public to use everyday vocabulary to search for and retrieve criminal judgments. The experimental results indicate that our method can help the general public, who are not familiar with professional legal terms, to acquire relevant criminal judgments more accurately and effectively.
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.2, S.280-290

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