Search (2 results, page 1 of 1)

0.0030283553 = product of:
  0.021198487 = sum of:
    0.021198487 = weight(_text_:with in 941) [ClassicSimilarity], result of:
      0.021198487 = score(doc=941,freq=4.0), product of:
        0.09383348 = queryWeight, product of:
          2.409771 = idf(docFreq=10797, maxDocs=44218)
          0.038938753 = queryNorm
        0.22591603 = fieldWeight in 941, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.409771 = idf(docFreq=10797, maxDocs=44218)
          0.046875 = fieldNorm(doc=941)
  0.14285715 = coord(1/7)

Abstract

We argue that the quality of a summary can be evaluated based on how many concepts in the original document(s) that can be preserved after summarization. Here, a concept refers to an abstract or concrete entity or its action often expressed by diverse terms in text. Summary generation can thus be considered as an optimization problem of selecting a set of sentences with minimal answer loss. In this paper, we propose a document concept lattice that indexes the hierarchy of local topics tied to a set of frequent concepts and the corresponding sentences containing these topics. The local topics will specify the promising sub-spaces related to the selected concepts and sentences. Based on this lattice, the summary is an optimized selection of a set of distinct and salient local topics that lead to maximal coverage of concepts with the given number of sentences. Our summarizer based on the concept lattice has demonstrated competitive performance in Document Understanding Conference 2005 and 2006 evaluations as well as follow-on tests.

0.0014275803 = product of:
  0.009993061 = sum of:
    0.009993061 = weight(_text_:with in 2804) [ClassicSimilarity], result of:
      0.009993061 = score(doc=2804,freq=2.0), product of:
        0.09383348 = queryWeight, product of:
          2.409771 = idf(docFreq=10797, maxDocs=44218)
          0.038938753 = queryNorm
        0.10649783 = fieldWeight in 2804, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.409771 = idf(docFreq=10797, maxDocs=44218)
          0.03125 = fieldNorm(doc=2804)
  0.14285715 = coord(1/7)

Content

Vgl. auch: Automatic Term Recognition (ATR) is a research task that deals with the identification of domain-specific terms. Terms, in simple words, are textual realization of significant concepts in an expertise domain. Additionally, domain-specific terms may be classified into a number of categories, in which each category represents a significant concept. A term classification task is often defined on top of an ATR procedure to perform such categorization. For instance, in the biomedical domain, terms can be classified as drugs, proteins, and genes. This is a reference dataset for terminology extraction and classification research in computational linguistics. It is a set of manually annotated terms in English language that are extracted from the ACL Anthology Reference Corpus (ACL ARC). The ACL ARC is a canonicalised and frozen subset of scientific publications in the domain of Human Language Technologies (HLT). It consists of 10,921 articles from 1965 to 2006. The dataset, called ACL RD-TEC, is comprised of more than 69,000 candidate terms that are manually annotated as valid and invalid terms. Furthermore, valid terms are classified as technology and non-technology terms. Technology terms refer to a method, process, or in general a technological concept in the domain of HLT, e.g. machine translation, word sense disambiguation, and language modelling. On the other hand, non-technology terms refer to important concepts other than technological; examples of such terms in the domain of HLT are multilingual lexicon, corpora, word sense, and language model. The dataset is created to serve as a gold standard for the comparison of the algorithms of term recognition and classification. [http://catalog.elra.info/product_info.php?products_id=1236].