-
Jones, S.; Paynter, G.W.: Automatic extractionof document keyphrases for use in digital libraries : evaluations and applications (2002)
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- Abstract
- This article describes an evaluation of the Kea automatic keyphrase extraction algorithm. Document keyphrases are conventionally used as concise descriptors of document content, and are increasingly used in novel ways, including document clustering, searching and browsing interfaces, and retrieval engines. However, it is costly and time consuming to manually assign keyphrases to documents, motivating the development of tools that automatically perform this function. Previous studies have evaluated Kea's performance by measuring its ability to identify author keywords and keyphrases, but this methodology has a number of well-known limitations. The results presented in this article are based on evaluations by human assessors of the quality and appropriateness of Kea keyphrases. The results indicate that, in general, Kea produces keyphrases that are rated positively by human assessors. However, typical Kea settings can degrade performance, particularly those relating to keyphrase length and domain specificity. We found that for some settings, Kea's performance is better than that of similar systems, and that Kea's ranking of extracted keyphrases is effective. We also determined that author-specified keyphrases appear to exhibit an inherent ranking, and that they are rated highly and therefore suitable for use in training and evaluation of automatic keyphrasing systems.
-
Advances in intelligent retrieval: Proc. of a conference ... Wadham College, Oxford, 16.-17.4.1985 (1986)
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- Content
- Enthält die Beiträge: ADDIS, T.: Extended relational analysis: a design approach to knowledge-based systems; PARKINSON, D.: Supercomputers and non-numeric processing; McGREGOR, D.R. u. J.R. MALONE: An architectural approach to advances in information retrieval; ALLEN, M.J. u. O.S. HARRISON: Word processing and information retrieval: some practical problems; MURTAGH, F.: Clustering and nearest neighborhood searching; ENSER, P.G.B.: Experimenting with the automatic classification of books; TESKEY, N. u. Z. RAZAK: An analysis of ranking for free text retrieval systems; ZARRI, G.P.: Interactive information retrieval: an artificial intelligence approach to deal with biographical data; HANCOX, P. u. F. SMITH: A case system processor for the PRECIS indexing language; ROUAULT, J.: Linguistic methods in information retrieval systems; ARAGON-RAMIREZ, V. u. C.D. PAICE: Design of a system for the online elucidation of natural language search statements; BROOKS, H.M., P.J. DANIELS u. N.J. BELKIN: Problem descriptions and user models: developing an intelligent interface for document retrieval systems; BLACK, W.J., P. HARGREAVES u. P.B. MAYES: HEADS: a cataloguing advisory system; BELL, D.A.: An architecture for integrating data, knowledge, and information bases
-
Liu, G.Z.: Semantic vector space model : implementation and evaluation (1997)
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- Abstract
- Presents the Semantic Vector Space Model (SVSM), a text representation and searching technique based on the combination of Vector Space Model (VSM) with heuristic syntax parsing and distributed representation of semantic case structures. Both document and queries are represented as semantic matrices. A search mechanism is designed to compute the similarity between 2 semantic matrices to predict relevancy. A prototype system was built to implement this model by modifying the SMART system and using the Xerox Part of Speech tagged as the pre-processor of the indexing. The prototype system was used in an experimental study to evaluate this technique in terms of precision, recall, and effectiveness of relevance ranking. Results show that if documents and queries were too short, the technique was less effective than VSM. But with longer documents and queires, especially when original docuemtns were used as queries, the system based on this technique was found be performance better than SMART
-
MacDougall, S.: Rethinking indexing : the impact of the Internet (1996)
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- Abstract
- Considers the challenge to professional indexers posed by the Internet. Indexing and searching on the Internet appears to have a retrograde step, as well developed and efficient information retrieval techniques have been replaced by cruder techniques, involving automatic keyword indexing and frequency ranking, leading to large retrieval sets and low precision. This is made worse by the apparent acceptance of this poor perfromance by Internet users and the feeling, on the part of indexers, that they are being bypassed by the producers of these hyperlinked menus and search engines. Key issues are: how far 'human' indexing will still be required in the Internet environment; how indexing techniques will have to change to stay relevant; and the future role of indexers. The challenge facing indexers is to adapt their skills to suit the online environment and to convince publishers of the need for efficient indexes on the Internet
-
Mao, J.; Xu, W.; Yang, Y.; Wang, J.; Yuille, A.L.: Explain images with multimodal recurrent neural networks (2014)
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- Abstract
- In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel sentence descriptions to explain the content of images. It directly models the probability distribution of generating a word given previous words and the image. Image descriptions are generated by sampling from this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on three benchmark datasets (IAPR TC-12 [8], Flickr 8K [28], and Flickr 30K [13]). Our model outperforms the state-of-the-art generative method. In addition, the m-RNN model can be applied to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval.
-
Karpathy, A.; Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions (2015)
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- Abstract
- We present a model that generates free-form natural language descriptions of image regions. Our model leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between text and visual data. Our approach is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate the effectiveness of our alignment model with ranking experiments on Flickr8K, Flickr30K and COCO datasets, where we substantially improve on the state of the art. We then show that the sentences created by our generative model outperform retrieval baselines on the three aforementioned datasets and a new dataset of region-level annotations.
-
Wolfekuhler, M.R.; Punch, W.F.: Finding salient features for personal Web pages categories (1997)
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- Date
- 1. 8.1996 22:08:06
- Source
- Computer networks and ISDN systems. 29(1997) no.8, S.1147-1156
-
SIGIR'92 : Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (1992)
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- Content
- HARMAN, D.: Relevance feedback revisited; AALBERSBERG, I.J.: Incremental relevance feedback; TAGUE-SUTCLIFFE, J.: Measuring the informativeness of a retrieval process; LEWIS, D.D.: An evaluation of phrasal and clustered representations on a text categorization task; BLOSSEVILLE, M.J., G. HÉBRAIL, M.G. MONTEIL u. N. PÉNOT: Automatic document classification: natural language processing, statistical analysis, and expert system techniques used together; MASAND, B., G. LINOFF u. D. WALTZ: Classifying news stories using memory based reasoning; KEEN, E.M.: Term position ranking: some new test results; CROUCH, C.J. u. B. YANG: Experiments in automatic statistical thesaurus construction; GREFENSTETTE, G.: Use of syntactic context to produce term association lists for text retrieval; ANICK, P.G. u. R.A. FLYNN: Versioning of full-text information retrieval system; BURKOWSKI, F.J.: Retrieval activities in a database consisting of heterogeneous collections; DEERWESTER, S.C., K. WACLENA u. M. LaMAR: A textual object management system; NIE, J.-Y.:Towards a probabilistic modal logic for semantic-based information retrieval; WANG, A.W., S.K.M. WONG u. Y.Y. YAO: An analysis of vector space models based on computational geometry; BARTELL, B.T., G.W. COTTRELL u. R.K. BELEW: Latent semantic indexing is an optimal special case of multidimensional scaling; GLAVITSCH, U. u. P. SCHÄUBLE: A system for retrieving speech documents; MARGULIS, E.L.: N-Poisson document modelling; HESS, M.: An incrementally extensible document retrieval system based on linguistics and logical principles; COOPER, W.S., F.C. GEY u. D.P. DABNEY: Probabilistic retrieval based on staged logistic regression; FUHR, N.: Integration of probabilistic fact and text retrieval; CROFT, B., L.A. SMITH u. H. TURTLE: A loosely-coupled integration of a text retrieval system and an object-oriented database system; DUMAIS, S.T. u. J. NIELSEN: Automating the assignement of submitted manuscripts to reviewers; GOST, M.A. u. M. MASOTTI: Design of an OPAC database to permit different subject searching accesses; ROBERTSON, A.M. u. P. WILLETT: Searching for historical word forms in a database of 17th century English text using spelling correction methods; FAX, E.A., Q.F. CHEN u. L.S. HEATH: A faster algorithm for constructing minimal perfect hash functions; MOFFAT, A. u. J. ZOBEL: Parameterised compression for sparse bitmaps; GRANDI, F., P. TIBERIO u. P. Zezula: Frame-sliced patitioned parallel signature files; ALLEN, B.: Cognitive differences in end user searching of a CD-ROM index; SONNENWALD, D.H.: Developing a theory to guide the process of designing information retrieval systems; CUTTING, D.R., J.O. PEDERSEN, D. KARGER, u. J.W. TUKEY: Scatter/ Gather: a cluster-based approach to browsing large document collections; CHALMERS, M. u. P. CHITSON: Bead: Explorations in information visualization; WILLIAMSON, C. u. B. SHNEIDERMAN: The dynamic HomeFinder: evaluating dynamic queries in a real-estate information exploring system
-
Salton, G.; Yang, C.S.: On the specification of term values in automatic indexing (1973)
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- Source
- Journal of documentation. 29(1973), S.351-372
-
Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986)
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- Information processing and management. 22(1986) no.6, S.465-476
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Hlava, M.M.K.: Automatic indexing : comparing rule-based and statistics-based indexing systems (2005)
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- Information outlook. 9(2005) no.8, S.22-23
-
Salton, G.: Another look at automatic text-retrieval systems (1986)
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- Communications of the Association for Computing Machinery. 29(1986), S.648-656
-
Salton, G.; Allan, J.; Buckley, C.; Singhal, A.: Automatic analysis, theme generation, and summarization of machine readable texts (1994)
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- Date
- 16. 8.1998 12:30:29
-
Biebricher, N.; Fuhr, N.; Lustig, G.; Schwantner, M.; Knorz, G.: ¬The automatic indexing system AIR/PHYS : from research to application (1988)
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- 16. 8.1998 12:51:22
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Kutschekmanesch, S.; Lutes, B.; Moelle, K.; Thiel, U.; Tzeras, K.: Automated multilingual indexing : a synthesis of rule-based and thesaurus-based methods (1998)
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- Information und Märkte: 50. Deutscher Dokumentartag 1998, Kongreß der Deutschen Gesellschaft für Dokumentation e.V. (DGD), Rheinische Friedrich-Wilhelms-Universität Bonn, 22.-24. September 1998. Hrsg. von Marlies Ockenfeld u. Gerhard J. Mantwill
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Stankovic, R. et al.: Indexing of textual databases based on lexical resources : a case study for Serbian (2016)
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- 1. 2.2016 18:25:22
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Koryconski, C.; Newell, A.F.: Natural-language processing and automatic indexing (1990)
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- Source
- Indexer. 17(1990), S.21-29
-
Frants, V.I.; Kamenoff, N.I.; Shapiro, J.: ¬One approach to classification of users and automatic clustering of documents (1993)
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- Information processing and management. 29(1993) no.2, S.187-195
-
Haas, S.; He, S.: Toward the automatic identification of sublanguage vocabulary (1993)
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- Source
- Information processing and management. 29(1993) no.6, S.721-744
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Molto, M.: Improving full text search performance through textual analysis (1993)
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- Source
- Information processing and management. 29(1993) no.5, S.614-632
