Learning Similarity Functions in Information Retrieval

Mandl, Thomas Learning Similarity Functions in Information Retrieval., 1998 . In EUFIT ‘98. 6th European Congress on Intelligent Techniques and Soft Computing. ,, Aachen, Germany, 8.-10.September 1998. [Conference paper]

[thumbnail of Mandl_Eufit_98.pdf]
Preview
 PDF
Mandl_Eufit_98.pdf
Download (72kB) | Preview

English abstract

Most models for Information Retrieval (IR) using neural networks are simple spreading activation models. Some of them were successfully applied to real world document collections. Nevertheless, they do not exploit the subsymbolic paradigma of neural processing. In this paper a model using a simple backpropagation network for IR is proposed. The COSIMIR model implements the central process in IR. It is a backpropagation network which calculates the similarity between a document and a query representation. The similarity function is learned through examples. Hence, it implements a cognitive similarity function. The first evaluation demonstrates that COSIMIR works well for short vectors.

Item type: Conference paper
Keywords: neural networks, information retrieval
Subjects: I. Information treatment for information services > IB. Content analysis (A and I, class.)
L. Information technology and library technology > LM. Automatic text retrieval.
L. Information technology and library technology > LS. Search engines.
Depositing user: Thomas Mandl
Date deposited: 29 Aug 2006
Last modified: 02 Oct 2014 12:04
URI: http://hdl.handle.net/10760/8042

References

Bordogna, Gloria; Pasi, Gabriella; Petrosino, Alfredo (1996): Relevance Feedback Based on a Neural Network. In: Zimmermann (ed.) (1996). pp. 846-849.

Boughanem, M.; Soulé-Dupuy, C. (1997): MercureO2: adhoc and routing tasks. In: Harman 1997.

Caid, William R.; Dumais, Susan T.; Gallant, Stephen I. (1995): Learned Vector-Space Models for Document Retrieval. In: Informatin Processing & Management. vol. 31 (3).1995. pp. 419-429.

Chen, Hsinchun (1995): Machine Learning for Information Rerieval: Neural Networks, Symbolic Learing, and Genetic Algorithmus. In: Journal of the American Society for Information Science. JASIS vol. 46(3). pp. 194-216.

Crestani, Fabio; Rijsbergen, Cornelis van (1997): A Model for Adaptive Information Retrieval. In: Journal of Intelligent Information Systems.

Doszkocs, T.E.; Reggia, J.; Lin, X. (1990): Connectionist Models and Information Retrieval. In: Annual Review of Information Science and Technology (ARIST), vol. 25. pp. 209-260.

Dumais, Susan (1994): Latent Semantic Indexing (LSI) and TREC-2. In: Harman 1994. pp. 105-115.

Escobedo, Richard; Smith, Scott; Caudell, Thomas (1993): A Neural Information Retrieval System. In: International Journal of Advanced Manufacturing Technology vol. 8 (4). pp. 269-274.

Harman, Donna (ed.) (1996): The Fourth Text Retrieval Conference (TREC-4).

Harman, Donna (ed.) (1997): The Fifth Text Retrieval Conference (TREC-5).

Harman, Donna (ed.) (1998): The Sixth Text Retrieval Conference (TREC-6).

Hartung, Joachim (1984): Lehr- und Handbuch der angewandten Statistik. München, Wien.

Jones, William; Furnas, George (1987): Pictures of Relevance: A geometric Analysis of Similarity Measures. In:

Journal of the American Society for Information Science. JASIS vol. 38(6). pp. 420-442.

Krause, Jürgen; Christa Womser-Hacker (eds.) (1997): Vages Information Retrieval und graphische Benutzungsoberflächen - Beispiel Werkstoffinformation. Konstanz.

Kwok, K.L.; Grunfeld, L. (1996): TREC-4 Ad-Hoc, Routing Retrieval and Filtering Experiments using PIRCS. In: Harman 1996.

Layaida, Redouane; Caron, Armand (1994): Applications of the Backpropagation Algorithm to an Information Retrieval System. In: Proceedings of the RIAO ‘94 (Rechenche d’Information assistée par Ordinateur). Rockfeller University. New York. pp. 161-171.

Ludwig, Michaela; Mandl, Thomas (1997): Ähnlichkeit von Werkstoffen: Die Anwendung unterschiedlicher Wissensmodellierungstechniken für eine intelligente Komponente. In: Krause/Womser-Hacker (1997). pp. 169-184.

Mandl, Thomas (1998): Das COSIMIR-Modell: Information Retrieval mit Neuronalen Netzen. Informationszentrum Sozialwissenschaften Bonn, Arbeitsbericht, Feb. 1998.

Mothe, Josiane (1994): Search Mechanisms Using a Neural Network Model. In: Proceedings of the RIAO 94 (Recherche d’Information assistée par Ordinateur). Rockfeller University. New York. pp. 275-294.

Salton, Gerard; Buckley, Chris (1988): On the Use of Spreading Activation Methods in Automatic Information Retrieval. In: Chiaramella, Yves (ed.): 11th Int. Conf. on Information Retrieval. New York 1988. pp. 147-160.

Scheinost, Ulrich; Haas, Hansjörg; Krause, Jürgen; Lindlbauer, Jürg (eds.) (1998): Marktanalyse und Marktprognose: Das ZVEI Verbandsinformationssystem ELVIRA. Bonn.

Wilkinson, Ross; Hingston, P. (1992): Incorporating the vector space model in a neural network used for document retrieval. In: Library HiTech News vol. 10 (1-2). pp. 69-75.

Womser-Hacker, Christa (1996): Das MIMOR-Modell. Mehrfachindexierung zur dynamischen Methoden-Objekt-Relationierung im Information Retrieval. Habilitationsschrift. Universität Regensburg, Informationswissenschaft.

Zimmermann, Hans-Jürgen (ed.): EUFIT ‘96. 4th European Congress on Intelligent Techniques and Soft Computing. Aachen, Germany, 02.-05. Sept. 1996.


Downloads

Downloads per month over past year

Actions (login required)

View Item View Item
Contact us
Send email Sponsors
Sponsor: AIMS Sponsor: D&P Sponsor: UniNa CAB Get connected
Facebook Facebook Twitter Twitter Linkedin LinkedIn RSS RSS