Mapping speech streams to conceptual structures

• Autorzy: Melz R. , Biemann C. , Böhm K. , Heyer G. , Schmidt F.
• Konferencja: Human Language Technologies as a challenge for Computer Science and Linguistics (2; 21-23.04.2005; Poznań, Poland)
• Języki publikacji: EN

Abstrakty

EN

We describe a software system that processes textual data and spoken input streams of natural language and arranges the information in a meaningful way on the screen: concepts as nodes, relations as edges. For spoken input, the software simulates conceptual awareness. A naturally spoken speech stream is converted into a word stream (speech-to-text), the most significant concepts are extracted and associated to related concepts, which have not been mentioned by the speaker(s) yet. The result is displayed on a screen as a conceptual structure.

Słowa kluczowe

EN

statistical language processing; co-occurrences; conceptual graphs; speech

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2005
• Tom: Vol. 15, no. 3
• Strony: 359--370
• Opis fizyczny: Bibliogr. 13 poz., rys.

Twórcy

autor

Melz R.

• Institute of Computer Science, University of Leipzig, Augustsolatz 10-11, 04109 Leipzig, Germany

autor

Biemann C.

• Institute of Computer Science, University of Leipzig, Augustsolatz 10-11, 04109 Leipzig, Germany

autor

Böhm K.

• Institute of Computer Science, University of Leipzig, Augustsolatz 10-11, 04109 Leipzig, Germany

autor

Heyer G.

• Institute of Computer Science, University of Leipzig, Augustsolatz 10-11, 04109 Leipzig, Germany

autor

Schmidt F.

• Institute of Computer Science, University of Leipzig, Augustsolatz 10-11, 04109 Leipzig, Germany

Bibliografia

• [1] F. Schmidt: Automatische Ermiulung semantischer Zusammenhange lexikalischer Einheiten und deren graphischceDarstellung. Diplomarbei,. Universitat Leipzig. 2000.
• [2] E. Battista and T. Tollis: Graph Drawing - Algorithms for the Visualization of Graphs. Prentice Hall, New Jersey, 1999.
• [3] D. L. Nelson, V. M. McKinney, N. R. Gee [and] G. A. Janczura: Interpreting the influence of implicitly activated memories on recall and recognition. Psychological Review, 105(1998).
• [4] F. De Saussure: Cours de Linguistique Generale. Payot. Paris. (1916).
• [5] C. Biemann. S. Bordag, G. Heyer, U. Quasthoff and C.Wolff: language-independent Methods for Compiling Monolingual Lexical Data, hi Proc. CICLing, Springer. (2004).
• [6] D. Jurafsky and J. H. Martin: Speech and Language Processing. Prentice Hall. (2000).
• [7] G. Heyer, U. Quasthoff and T. Wittig: Wissensrohstoff Text. Subject to appear at W3L-Verlag. Dortmund. (2005).
• [8] G. K. Zipf: Human Behavior and the Principle of least Effort. Addison-Wesley Press. 1965.
• [9] J. F. Sowa: Conceptual Structures - Information Processing in Mind and Machine. Addison-Wesley. Massachusetts. 1984.
• [10] T. Harley: The Psychology of Language. Taylor & Francis Group, 2001.
• [11] M. Steyvers and J. B. Tenenbaum: The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth. Subject to appear in Cognitive Science, (2005).
• [12] R. Grishman: Information Extraction: Techniques and Challenges. SCIE, (1997), 10-27.
• [13] C. Biemann, G. Heyer, H. F. Witschel and F. Schmidt: Eine Wissenslandkarte der Knowtech. In T. Schildhauer N. Gronau, B. Petkoff (Eds), Wissensmanagement – Wandel, Wertschopfung, Wachstum, GITO. Berlin. (2004). s 207-216