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Martin, Andrea E.; McElree, Brian – Journal of Memory and Language, 2008
Interpreting a verb-phrase ellipsis (VP ellipsis) requires accessing an antecedent in memory, and then integrating a representation of this antecedent into the local context. We investigated the online interpretation of VP ellipsis in an eye-tracking experiment and four speed-accuracy tradeoff experiments. To investigate whether the antecedent for…
Descriptors: Verbs, Memory, Information Retrieval, Eye Movements
Zhu, Guangyu – ProQuest LLC, 2009
The nature and scope of available documents are changing significantly in many areas of document analysis and retrieval as complex, heterogeneous collections become accessible to virtually everyone via the web. The increasing level of diversity presents a great challenge for document image content categorization, indexing, and retrieval.…
Descriptors: Printed Materials, Measures (Individuals), Electronic Equipment, Dictionaries
Peer reviewedButterly, Elizabeth – Information Processing and Management, 1975
Reports on an evaluation of the construction of selective dissemination of information profiles for the International Nuclear Information System (INIS) in South Africa. (PF)
Descriptors: Information Retrieval, Information Systems, Relevance (Information Retrieval), Search Strategies
Peer reviewedFarradane, J. – Journal of Documentation, 1974
Methods of testing systems in practice and in theory are critically reviewed. Some new theoretical considerations are advanced. (Author)
Descriptors: Evaluation Methods, Information Retrieval, Measurement Techniques, Relevance (Information Retrieval)
Peer reviewedRobertson, S. E.; Belkin, N. J. – Journal of Documentation, 1978
Explores the possibility of combining the two principles of ranking according to degree of relevance, and ranking according to probability of relevance, for information retrieval systems, but concludes that, while neither is adequate alone, no single, all-embracing ranking principle can be constructed to replace the two. (Author/VT)
Descriptors: Information Retrieval, Information Systems, Relevance (Information Retrieval), Search Strategies
Peer reviewedRobertson, S. E.; Sparck Jones, K. – Journal of the American Society for Information Science, 1976
Examines statistical techniques for exploiting relevance information to weight search terms. These techniques are presented as a natural extension of weighting methods using information about the distribution of index terms in documents in general. (Author)
Descriptors: Indexing, Information Retrieval, Probability, Relevance (Information Retrieval)
Peer reviewedRaghavan, Vijay V.; Wong, S. K. M. – Journal of the American Society for Information Science, 1986
Presents notations and definitions necessary to identify the concepts and relationships that are important in modelling information retrieval objects and processes in the context of vector spaces. Earlier work on the use of vector models is evaluated in terms of the concepts introduced and certain problems are identified. (Author/EM)
Descriptors: Information Retrieval, Mathematical Models, Relevance (Information Retrieval), Vectors (Mathematics)
Peer reviewedSoergel, Dagobert – Journal of the American Society for Information Science, 1976
Instead of leaving the user at the point where he or she receives the information from the reference storage and retrieval system, we should follow him or her to see what the impact is from the information received, how it actually affects the user's performance in his or her task. (Author/PF)
Descriptors: Evaluation Methods, Information Retrieval, Measurement Techniques, Relevance (Information Retrieval)
Peer reviewedCleverdon, Cyril W. – Journal of Documentation, 1972
The inverse relationship of recall and precision is not a fundamental law. It must not be used as an excuse for failing to bring about any overall improvement in the performance of information retrieval systems (12 references) (Author/NH)
Descriptors: Information Retrieval, Information Systems, Relevance (Information Retrieval), Search Strategies
Peer reviewedCooper, William S. – Journal of the American Society for Information Science, 1973
A hypothetical methodology is presented for measuring retrieval effectiveness by using the personal utility, based on a user's subjective evaluation, of a retrieval system's output. Because the methodology is impractical, compromise methods are outlined and their underlying assumptions, which can serve as criteria for formulating appropriate…
Descriptors: Information Retrieval, Information Theory, Relevance (Information Retrieval), Research Methodology
Peer reviewedCanham, G. W. Rayner – Journal of Chemical Documentation, 1972
The difficulties of readily obtaining peripheral information and drawbacks of citation searches are discussed. A citation survey of inorganic chemistry journals is compared with previous studies. The publication of a chemical newspaper is proposed. (11 references) (Author)
Descriptors: Chemistry, Citation Indexes, Information Retrieval, Problems
Peer reviewedHarter, Stephen P. – Library Quarterly, 1971
The relevance assessments belonging to the Cranfield II document/query collection are shown to be faulty, in the sense that many" relevant documents were not so identified by the Cranfield judges. 9 references. (Author)
Descriptors: Bibliographic Coupling, Evaluation, Indexing, Information Retrieval
Peer reviewedBloomfield, Masse – Special Libraries, 1971
The indexing of Cranfield I and II is given and critical comments made of this indexing. Comparisons of Cranfield indexing to other types of indexing are made. (AB)
Descriptors: Indexes, Indexing, Information Retrieval, Permuted Indexes
Lancaster, F. W. – Amer Doc, 1969
The complete report, ERIC document ED 022 494, is abstracted in the February 1969 "Research in Education.
Descriptors: Evaluation, Indexing, Information Retrieval, Information Systems
van Rijsbergen, C. J. – Drexel Library Quarterly, 1978
Addresses the application of automatic classification methods to the problems associated with computerized document retrieval. Different kinds of classifications are described, and both document and term clustering methods are discussed. References and notes are provided. (Author/JD)
Descriptors: Cluster Grouping, Essays, Information Retrieval, Problems

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