Learning Semantic Constraints for the Automatic Discovery of Part-Whole Relations

Learning Semantic Constraints for the Automatic Discovery of Part-Whole Relations Roxana Girju Human Language Technology Research Institute, University of Texas at Dallas and Computer Science Department, Baylor University girju@ecs.baylor.edu Adriana Badulescu and Dan Moldovan Human Language Technology Research Institute, University of Texas at Dallas adriana@hlt.utdallas.edu, moldovan@utdallas.edu Abstract The discovery of semantic relations from text becomes increasingly important for applica- tions such as Question Answering, Informa- tion Extraction, Text Summarization, Text Un- derstanding, and others. The semantic rela- tions are detected by checking selectional con- straints. This paper presents a method and its results for learning semantic constraints to de- tect part-whole relations. Twenty constraints were found. Their validity was tested on a 10,000 sentence corpus, and the targeted part- whole relations were detected with an accuracy of 83%. 1 Introduction 1.1 Problem description An important semantic relation for several NLP applica- tions is the part-whole relation, or meronymy. Consider the text: The car’s mail messenger is busy at work in the mail car as the train moves along. Through the open side door of the car, moving scenery can be seen. The worker is alarmed when he hears an unusual sound. He peeks through the door’s keyhole leading to the tender and locomotive cab and sees the two ban- dits trying to break through the express car door. There are six part-whole relations in this text: 1) the mail car is part of the train, 2) the side door is part of the car, 3) the keyhole is part of the door, 4) the cab is part of the locomotive, 5) the door is part of the car, and 6) the car is part of the express train (the last two in the compound noun express car door). Understanding part-whole relations allows Question Answering systems to address questions such as “What are the components of X?, What is X made of? and others. Question Answering, Information Extraction and Text Summarization systems often need to identify relations between entities as well as synthesize information gath- ered from multiple documents. More and more knowl- edge intensive techniques are used to augment statistical methods when building advanced NLP applications. This paper provides a method for deriving semantic constraints necessary to discover part-whole relations. 1.2 Semantics of part-whole relation There are different ways in which we refer to something as being a part of something else, and this led many re- searchers to claim that meronymy is a complex relation that “should be treated as a collection of relations, not as a single relation” (Iris et al. , 1988). Based on linguistic and cognitive considerations about the way parts contribute to the structure of the wholes, Winston, Chaffin and Hermann (Winston et al. 1987) determined in 1987 six types of part-whole rela-, tions: Component-Integral object (wheel - car), Member- Collection (soldier - army), Portion-Mass (meter - kilo- meter), Stuff-Object (alcohol - wine), Feature-Activity (paying - shopping), and Place-Area (oasis - desert). The part-whole relations in WordNet are classified into three basic types: Member-of (e.g., …

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