Search (9 results, page 1 of 1)

0.0070806327 = product of:
  0.035403162 = sum of:
    0.035403162 = weight(_text_:22 in 3343) [ClassicSimilarity], result of:
      0.035403162 = score(doc=3343,freq=2.0), product of:
        0.18300882 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.052260913 = queryNorm
        0.19345059 = fieldWeight in 3343, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3343)
  0.2 = coord(1/5)

Date

16.11.2017 13:04:22

0.0070806327 = product of:
  0.035403162 = sum of:
    0.035403162 = weight(_text_:22 in 3524) [ClassicSimilarity], result of:
      0.035403162 = score(doc=3524,freq=2.0), product of:
        0.18300882 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.052260913 = queryNorm
        0.19345059 = fieldWeight in 3524, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3524)
  0.2 = coord(1/5)

Date

16.11.2017 13:30:22

0.006830811 = product of:
  0.034154054 = sum of:
    0.034154054 = weight(_text_:it in 4450) [ClassicSimilarity], result of:
      0.034154054 = score(doc=4450,freq=4.0), product of:
        0.15115225 = queryWeight, product of:
          2.892262 = idf(docFreq=6664, maxDocs=44218)
          0.052260913 = queryNorm
        0.22595796 = fieldWeight in 4450, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.892262 = idf(docFreq=6664, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4450)
  0.2 = coord(1/5)

Abstract

Web data repositories usually contain references to thousands of real-world entities from multiple sources. It is not uncommon that multiple entities share the same label (polysemes) and that distinct label variations are associated with the same entity (synonyms), which frequently leads to ambiguous interpretations. Further, spelling variants, acronyms, abbreviated forms, and misspellings compound to worsen the problem. Solving this problem requires identifying which labels correspond to the same real-world entity, a process known as entity resolution. One approach to solve the entity resolution problem is to associate an authority identifier and a list of variant forms with each entity-a data structure known as an authority file. In this work, we propose a generic framework for implementing a method for generating authority files. Our method uses information from the Web to improve the quality of the authority file and, because of that, is referred to as WER-Web-based Entity Resolution. Our contribution here is threefold: (a) we discuss how to implement the WER framework, which is flexible and easy to adapt to new domains; (b) we run extended experimentation with our WER framework to show that it outperforms selected baselines; and (c) we compare the results of a specialized solution for author name resolution with those produced by the generic WER framework, and show that the WER results remain competitive.

0.006830811 = product of:
  0.034154054 = sum of:
    0.034154054 = weight(_text_:it in 1184) [ClassicSimilarity], result of:
      0.034154054 = score(doc=1184,freq=4.0), product of:
        0.15115225 = queryWeight, product of:
          2.892262 = idf(docFreq=6664, maxDocs=44218)
          0.052260913 = queryNorm
        0.22595796 = fieldWeight in 1184, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.892262 = idf(docFreq=6664, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1184)
  0.2 = coord(1/5)

Abstract

Learning to rank (L2R) algorithms use a labeled training set to generate a ranking model that can later be used to rank new query results. These training sets are costly and laborious to produce, requiring human annotators to assess the relevance or order of the documents in relation to a query. Active learning algorithms are able to reduce the labeling effort by selectively sampling an unlabeled set and choosing data instances that maximize a learning function's effectiveness. In this article, we propose a novel two-stage active learning method for L2R that combines and exploits interesting properties of its constituent parts, thus being effective and practical. In the first stage, an association rule active sampling algorithm is used to select a very small but effective initial training set. In the second stage, a query-by-committee strategy trained with the first-stage set is used to iteratively select more examples until a preset labeling budget is met or a target effectiveness is achieved. We test our method with various LETOR benchmarking data sets and compare it with several baselines to show that it achieves good results using only a small portion of the original training sets.

0.004830113 = product of:
  0.024150565 = sum of:
    0.024150565 = weight(_text_:it in 2531) [ClassicSimilarity], result of:
      0.024150565 = score(doc=2531,freq=2.0), product of:
        0.15115225 = queryWeight, product of:
          2.892262 = idf(docFreq=6664, maxDocs=44218)
          0.052260913 = queryNorm
        0.15977642 = fieldWeight in 2531, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.892262 = idf(docFreq=6664, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2531)
  0.2 = coord(1/5)

Abstract

It is well known that links are an important source of information when dealing with Web collections. However, the question remains on whether the same techniques that are used on the Web can be applied to collections of documents containing citations between scientific papers. In this work we present a comparative study of digital library citations and Web links, in the context of automatic text classification. We show that there are in fact differences between citations and links in this context. For the comparison, we run a series of experiments using a digital library of computer science papers and a Web directory. In our reference collections, measures based on co-citation tend to perform better for pages in the Web directory, with gains up to 37% over text based classifiers, while measures based on bibliographic coupling perform better in a digital library. We also propose a simple and effective way of combining a traditional text based classifier with a citation-link based classifier. This combination is based on the notion of classifier reliability and presented gains of up to 14% in micro-averaged F1 in the Web collection. However, no significant gain was obtained in the digital library. Finally, a user study was performed to further investigate the causes for these results. We discovered that misclassifications by the citation-link based classifiers are in fact difficult cases, hard to classify even for humans.

0.004830113 = product of:
  0.024150565 = sum of:
    0.024150565 = weight(_text_:it in 2848) [ClassicSimilarity], result of:
      0.024150565 = score(doc=2848,freq=2.0), product of:
        0.15115225 = queryWeight, product of:
          2.892262 = idf(docFreq=6664, maxDocs=44218)
          0.052260913 = queryNorm
        0.15977642 = fieldWeight in 2848, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.892262 = idf(docFreq=6664, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2848)
  0.2 = coord(1/5)

Abstract

In this article we present FLUX-CiM, a novel method for extracting components (e.g., author names, article titles, venues, page numbers) from bibliographic citations. Our method does not rely on patterns encoding specific delimiters used in a particular citation style. This feature yields a high degree of automation and flexibility, and allows FLUX-CiM to extract from citations in any given format. Differently from previous methods that are based on models learned from user-driven training, our method relies on a knowledge base automatically constructed from an existing set of sample metadata records from a given field (e.g., computer science, health sciences, social sciences, etc.). These records are usually available on the Web or other public data repositories. To demonstrate the effectiveness and applicability of our proposed method, we present a series of experiments in which we apply it to extract bibliographic data from citations in articles of different fields. Results of these experiments exhibit precision and recall levels above 94% for all fields, and perfect extraction for the large majority of citations tested. In addition, in a comparison against a state-of-the-art information-extraction method, ours produced superior results without the training phase required by that method. Finally, we present a strategy for using bibliographic data resulting from the extraction process with FLUX-CiM to automatically update and expand the knowledge base of a given domain. We show that this strategy can be used to achieve good extraction results even if only a very small initial sample of bibliographic records is available for building the knowledge base.

0.004830113 = product of:
  0.024150565 = sum of:
    0.024150565 = weight(_text_:it in 3986) [ClassicSimilarity], result of:
      0.024150565 = score(doc=3986,freq=2.0), product of:
        0.15115225 = queryWeight, product of:
          2.892262 = idf(docFreq=6664, maxDocs=44218)
          0.052260913 = queryNorm
        0.15977642 = fieldWeight in 3986, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.892262 = idf(docFreq=6664, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3986)
  0.2 = coord(1/5)

Abstract

Name ambiguity in the context of bibliographic citations is a difficult problem which, despite the many efforts from the research community, still has a lot of room for improvement. In this article, we present a heuristic-based hierarchical clustering method to deal with this problem. The method successively fuses clusters of citations of similar author names based on several heuristics and similarity measures on the components of the citations (e.g., coauthor names, work title, and publication venue title). During the disambiguation task, the information about fused clusters is aggregated providing more information for the next round of fusion. In order to demonstrate the effectiveness of our method, we ran a series of experiments in two different collections extracted from real-world digital libraries and compared it, under two metrics, with four representative methods described in the literature. We present comparisons of results using each considered attribute separately (i.e., coauthor names, work title, and publication venue title) with the author name attribute and using all attributes together. These results show that our unsupervised method, when using all attributes, performs competitively against all other methods, under both metrics, loosing only in one case against a supervised method, whose result was very close to ours. Moreover, such results are achieved without the burden of any training and without using any privileged information such as knowing a priori the correct number of clusters.

0.004830113 = product of:
  0.024150565 = sum of:
    0.024150565 = weight(_text_:it in 4990) [ClassicSimilarity], result of:
      0.024150565 = score(doc=4990,freq=2.0), product of:
        0.15115225 = queryWeight, product of:
          2.892262 = idf(docFreq=6664, maxDocs=44218)
          0.052260913 = queryNorm
        0.15977642 = fieldWeight in 4990, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.892262 = idf(docFreq=6664, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4990)
  0.2 = coord(1/5)

Abstract

Sentiment analysis has become a very important tool for analysis of social media data. There are several methods developed, covering distinct aspects of the problem and disparate strategies. However, no single technique fits well in all cases or for all data sources. Supervised approaches may be able to adapt to specific situations, but require manually labeled training, which is very cumbersome and expensive to acquire, mainly for a new application. In this context, we propose to combine several popular and effective state-of-the-practice sentiment analysis methods by means of an unsupervised bootstrapped strategy. One of our main goals is to reduce the large variability (low stability) of the unsupervised methods across different domains. The experimental results demonstrate that our combined method (aka, 10SENT) improves the effectiveness of the classification task, considering thirteen different data sets. Also, it tackles the key problem of cross-domain low stability and produces the best (or close to best) results in almost all considered contexts, without any additional costs (e.g., manual labeling). Finally, we also investigate a transfer learning approach for sentiment analysis to gather additional (unsupervised) information for the proposed approach, and we show the potential of this technique to improve our results.

0.004830113 = product of:
  0.024150565 = sum of:
    0.024150565 = weight(_text_:it in 5291) [ClassicSimilarity], result of:
      0.024150565 = score(doc=5291,freq=2.0), product of:
        0.15115225 = queryWeight, product of:
          2.892262 = idf(docFreq=6664, maxDocs=44218)
          0.052260913 = queryNorm
        0.15977642 = fieldWeight in 5291, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.892262 = idf(docFreq=6664, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5291)
  0.2 = coord(1/5)

Abstract

Text representation models are the fundamental basis for information retrieval and text mining tasks. Although different text models have been proposed, they typically target specific task aspects in isolation, such as time efficiency, accuracy, or applicability for different scenarios. Here we present Bag of Textual Graphs (BoTG), a general text representation model that addresses these three requirements at the same time. The proposed textual representation is based on a graph-based scheme that encodes term proximity and term ordering, and represents text documents into an efficient vector space that addresses all these aspects as well as provides discriminative textual patterns. Extensive experiments are conducted in two experimental scenarios-classification and retrieval-considering multiple well-known text collections. We also compare our model against several methods from the literature. Experimental results demonstrate that our model is generic enough to handle different tasks and collections. It is also more efficient than the widely used state-of-the-art methods in textual classification and retrieval tasks, with a competitive effectiveness, sometimes with gains by large margins.