Search (41 results, page 1 of 3)

0.02475569 = product of:
  0.04951138 = sum of:
    0.04951138 = product of:
      0.09902276 = sum of:
        0.09902276 = weight(_text_:22 in 4577) [ClassicSimilarity], result of:
          0.09902276 = score(doc=4577,freq=2.0), product of:
            0.18281296 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052204985 = queryNorm
            0.5416616 = fieldWeight in 4577, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.109375 = fieldNorm(doc=4577)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

2. 4.2000 18:01:22

0.021219164 = product of:
  0.04243833 = sum of:
    0.04243833 = product of:
      0.08487666 = sum of:
        0.08487666 = weight(_text_:22 in 6783) [ClassicSimilarity], result of:
          0.08487666 = score(doc=6783,freq=2.0), product of:
            0.18281296 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052204985 = queryNorm
            0.46428138 = fieldWeight in 6783, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.09375 = fieldNorm(doc=6783)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Footnote

A special issue of selected papers from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'97), held Singapore, 22-23 Feb 1997

0.018526107 = product of:
  0.037052214 = sum of:
    0.037052214 = sum of:
      0.01583305 = weight(_text_:retrieval in 1833) [ClassicSimilarity], result of:
        0.01583305 = score(doc=1833,freq=2.0), product of:
          0.15791564 = queryWeight, product of:
            3.024915 = idf(docFreq=5836, maxDocs=44218)
            0.052204985 = queryNorm
          0.10026272 = fieldWeight in 1833, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.024915 = idf(docFreq=5836, maxDocs=44218)
            0.0234375 = fieldNorm(doc=1833)
      0.021219164 = weight(_text_:22 in 1833) [ClassicSimilarity], result of:
        0.021219164 = score(doc=1833,freq=2.0), product of:
          0.18281296 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052204985 = queryNorm
          0.116070345 = fieldWeight in 1833, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0234375 = fieldNorm(doc=1833)
  0.5 = coord(1/2)

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

0.017454296 = product of:
  0.034908593 = sum of:
    0.034908593 = product of:
      0.069817185 = sum of:
        0.069817185 = weight(_text_:retrieval in 607) [ClassicSimilarity], result of:
          0.069817185 = score(doc=607,freq=14.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.442117 = fieldWeight in 607, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=607)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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"

0.017454296 = product of:
  0.034908593 = sum of:
    0.034908593 = product of:
      0.069817185 = sum of:
        0.069817185 = weight(_text_:retrieval in 3607) [ClassicSimilarity], result of:
          0.069817185 = score(doc=3607,freq=14.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.442117 = fieldWeight in 3607, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3607)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

0.01583305 = product of:
  0.0316661 = sum of:
    0.0316661 = product of:
      0.0633322 = sum of:
        0.0633322 = weight(_text_:retrieval in 1067) [ClassicSimilarity], result of:
          0.0633322 = score(doc=1067,freq=8.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.40105087 = fieldWeight in 1067, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=1067)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

0.014536826 = product of:
  0.029073652 = sum of:
    0.029073652 = sum of:
      0.014927543 = weight(_text_:retrieval in 1789) [ClassicSimilarity], result of:
        0.014927543 = score(doc=1789,freq=4.0), product of:
          0.15791564 = queryWeight, product of:
            3.024915 = idf(docFreq=5836, maxDocs=44218)
            0.052204985 = queryNorm
          0.09452859 = fieldWeight in 1789, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            3.024915 = idf(docFreq=5836, maxDocs=44218)
            0.015625 = fieldNorm(doc=1789)
      0.014146109 = weight(_text_:22 in 1789) [ClassicSimilarity], result of:
        0.014146109 = score(doc=1789,freq=2.0), product of:
          0.18281296 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052204985 = queryNorm
          0.07738023 = fieldWeight in 1789, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.015625 = fieldNorm(doc=1789)
  0.5 = coord(1/2)

Date

23. 3.2008 19:10:22

RSWK

Information Retrieval (BVB)

Subject

Information Retrieval (BVB)

0.014146109 = product of:
  0.028292218 = sum of:
    0.028292218 = product of:
      0.056584436 = sum of:
        0.056584436 = weight(_text_:22 in 1737) [ClassicSimilarity], result of:
          0.056584436 = score(doc=1737,freq=2.0), product of:
            0.18281296 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052204985 = queryNorm
            0.30952093 = fieldWeight in 1737, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=1737)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22.11.1998 18:57:22

0.014146109 = product of:
  0.028292218 = sum of:
    0.028292218 = product of:
      0.056584436 = sum of:
        0.056584436 = weight(_text_:22 in 4261) [ClassicSimilarity], result of:
          0.056584436 = score(doc=4261,freq=2.0), product of:
            0.18281296 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052204985 = queryNorm
            0.30952093 = fieldWeight in 4261, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=4261)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

17. 7.2002 19:22:06

0.014146109 = product of:
  0.028292218 = sum of:
    0.028292218 = product of:
      0.056584436 = sum of:
        0.056584436 = weight(_text_:22 in 1270) [ClassicSimilarity], result of:
          0.056584436 = score(doc=1270,freq=2.0), product of:
            0.18281296 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052204985 = queryNorm
            0.30952093 = fieldWeight in 1270, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=1270)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Source

Information systems. 22(1997) nos.5/6, S.333-347

0.013711825 = product of:
  0.02742365 = sum of:
    0.02742365 = product of:
      0.0548473 = sum of:
        0.0548473 = weight(_text_:retrieval in 1326) [ClassicSimilarity], result of:
          0.0548473 = score(doc=1326,freq=6.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.34732026 = fieldWeight in 1326, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=1326)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

0.013194209 = product of:
  0.026388418 = sum of:
    0.026388418 = product of:
      0.052776836 = sum of:
        0.052776836 = weight(_text_:retrieval in 3723) [ClassicSimilarity], result of:
          0.052776836 = score(doc=3723,freq=2.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.33420905 = fieldWeight in 3723, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.078125 = fieldNorm(doc=3723)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Footnote

Übers. des Titels: Perspectives on the retrieval and exploitation of electronic information: data mining

0.0130616 = product of:
  0.0261232 = sum of:
    0.0261232 = product of:
      0.0522464 = sum of:
        0.0522464 = weight(_text_:retrieval in 4260) [ClassicSimilarity], result of:
          0.0522464 = score(doc=4260,freq=4.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.33085006 = fieldWeight in 4260, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4260)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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

0.0130616 = product of:
  0.0261232 = sum of:
    0.0261232 = product of:
      0.0522464 = sum of:
        0.0522464 = weight(_text_:retrieval in 600) [ClassicSimilarity], result of:
          0.0522464 = score(doc=600,freq=4.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.33085006 = fieldWeight in 600, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=600)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Footnote

Einführung in einen Themenschwerpunkt "Mining Web resources for enhancing information retrieval"

0.012377845 = product of:
  0.02475569 = sum of:
    0.02475569 = product of:
      0.04951138 = sum of:
        0.04951138 = weight(_text_:22 in 2908) [ClassicSimilarity], result of:
          0.04951138 = score(doc=2908,freq=2.0), product of:
            0.18281296 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052204985 = queryNorm
            0.2708308 = fieldWeight in 2908, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2908)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Source

Information systems. 22(1997) nos.5/6, S.349-385

0.0114265205 = product of:
  0.022853041 = sum of:
    0.022853041 = product of:
      0.045706082 = sum of:
        0.045706082 = weight(_text_:retrieval in 804) [ClassicSimilarity], result of:
          0.045706082 = score(doc=804,freq=6.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.28943354 = fieldWeight in 804, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=804)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

LCSH

Data mining ; Information retrieval

Subject

Data mining ; Information retrieval

0.0114265205 = product of:
  0.022853041 = sum of:
    0.022853041 = product of:
      0.045706082 = sum of:
        0.045706082 = weight(_text_:retrieval in 605) [ClassicSimilarity], result of:
          0.045706082 = score(doc=605,freq=6.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.28943354 = fieldWeight in 605, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=605)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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"

0.011195658 = product of:
  0.022391316 = sum of:
    0.022391316 = product of:
      0.04478263 = sum of:
        0.04478263 = weight(_text_:retrieval in 4716) [ClassicSimilarity], result of:
          0.04478263 = score(doc=4716,freq=4.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.2835858 = fieldWeight in 4716, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=4716)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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

0.011195658 = product of:
  0.022391316 = sum of:
    0.022391316 = product of:
      0.04478263 = sum of:
        0.04478263 = weight(_text_:retrieval in 91) [ClassicSimilarity], result of:
          0.04478263 = score(doc=91,freq=4.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.2835858 = fieldWeight in 91, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=91)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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

0.011195658 = product of:
  0.022391316 = sum of:
    0.022391316 = product of:
      0.04478263 = sum of:
        0.04478263 = weight(_text_:retrieval in 92) [ClassicSimilarity], result of:
          0.04478263 = score(doc=92,freq=4.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.2835858 = fieldWeight in 92, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=92)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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