Document (#31070)

Author

Thiel, M.

Title

Bedingt wahrscheinliche Syntaxbäume

Source

Information und Sprache: Beiträge zu Informationswissenschaft, Computerlinguistik, Bibliothekswesen und verwandten Fächern. Festschrift für Harald H. Zimmermann. Herausgegeben von Ilse Harms, Heinz-Dirk Luckhardt und Hans W. Giessen

Imprint

München : Saur

Year

2006

Pages

S.85-94

Abstract

Es wird argumentiert, dass die Ansätze der probabilistischen kontextfreien Grammatiken und anderer Modelle nicht ausreichend sind für die Lösung der im Parsing auftretenden Probleme. Zu deren Lösung wird folgende Hypothese aufgestellt: Die Wahrscheinlichkeiten einzelner Lesarten und verschiedener Knoten in einem Syntaxbaum sind voneinander abhängig. Vereindeutigt man eine Lesart bzw. einen Knoten, hat dies Auswirkungen auf die Wahrscheinlichkeit anderer Lesarten bzw. Knoten. Daher werden alle Lesarten und Syntaxbäume in einen Graphen integriert. Wenn die Wahrscheinlichkeiten also voneinander abhängig sind, wird angenommen, dass die Theorie der bedingten Wahrscheinlichkeiten von Bayes als Basis eine Lösung produzieren sollte. An einem Beispiel wird dies nachvollzogen und die Hypothese konnte bestätigt werden.
Die Tendenz ist eindeutig: wo immer es sinnvoll ist, werden hart' programmierte Lösungen durch Ansätze des Softcomputing ersetzt. Vor allem technische und kommerzielle Bereiche profitieren davon. So finden wir Kransteuerungen und viele andere Anwendungen mit Fuzzy Expertensystemen sowie Bilderkennungssysteme und Entscheidungen über die Kreditvergabe mit Neuronalen Netzen oder auch Methoden des Maschinellen Lernens (vgl. Jafar-Shaghaghi 1994). Ein Prinzip dieser Ansätze ist, dass die Software sich automatisch an die spezielle Situation und Datengrundlage der Anwendung anpasst. Flexibilität der Anpassung und die Fähigkeit zur Verallgemeinerung auf bislang ungesehene Fälle sind implizit in den Methoden vorhanden. Gerade dies ist auch ein typisches Problem, das bei der Beschreibung und vor allem beim Parsen natürlicher Sprache auftritt. Bei der Verarbeitung natürlicher Sprache kommt das leidige Problem der Ambiguität auf verschiedenen Ebenen hinzu. Alternative Regeln schließen sich in ihrer Anwendung in einem Satz meistens gegenseitig aus und sind nicht alle an der aktuellen Stelle gleich wahrscheinlich. Auf diese Problematik wurde schon früh hingewiesen (Thiel 1987, 137 ff.), wo versucht wurde, mit Gewichtungen die Wahrscheinlichkeit von Regeln, Syntaxbäumen, Kategorien und Wortsemantik in den Griff zu bekommen. Das Gewicht eines Syntaxbaumes kann z.B. einfach zugewiesen werden oder berechnet werden als Funktion des Baumes, aus dem er abgeleitet wird, und der angewandten Regel. Ein solches Verfahren wird (Thiel 1987, 152) am Beispiel einer Heuristik für die Inferenzmaschine eines Expertensystems gezeigt. Aber auch bereits in einer sehr frühen Veröffentlichung zur Analyse natürlicher Sprache, an der Zimmermann maßgeblich beteiligt war, wurde auf Vorkommenswahrscheinlichkeiten hingewiesen: "Statistische Auswertung von Typen des Satzbaus, Bau nominaler und verbaler Gruppen ..." (Eggers et al. 1969, 18). Derzeit konzentrieren sich die Ansätze von Vagheit in der Verarbeitung von natürlicher Sprache vor allem auf die Filterung von Texten z.B. in Spam-Filtern und auf probabilistische kontextfreie Grammatiken.

Theme

Computerlinguistik

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4. Czap, H.: Theorienbildung mittels Künstlich Neuronaler Netze vom Typ MLP (2000) 0.16

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5. Brocke, J. vom: Referenzmodellierung : Gestaltung und Verteilung von Konstruktionsprozessen (2003) 0.15

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