Capturing QP-relevant Information from Natural Language Text

Understanding descriptions of physical phenomena starts with the identification of continuous parameters that are involved in the physical processes. Descriptions of physical phenomena typically contain abundant references to physical quantities. The extraction of information about continuous parameters is therefore an essential step in building models of physical processes (Kuehne, 2003). People can learn about the physical world from textbooks and develop an understanding of physical phenomena from simple descriptions. As part of our ongoing investigation of the extraction and representation of knowledge about physical processes found in natural language text, we describe a natural language system that captures information about instances of physical processes from paragraph-sized descriptions through a deep semantic interpretation process as a set of interconnected frame structures. We have previously shown that natural language descriptions of physical phenomena can contain abundant QP-relevant information (Kuehne & Forbus, 2002). If one looks carefully at the descriptions of physical processes, given either as a concrete example or as generalized knowledge, one can identify parts of the natural language description that correspond to certain elements of QP Theory. In the same context, we have also proposed that QP theory can provide a knowledge representation language for aspects of natural language semantics concerned with continuous parameters and continuous causation. We have outlined a representational scheme that recasts the theoretical framework of QP theory in terms of frame semantics. QP frames use a representational scheme that is compatible with the notions of frames and frame elements in FrameNet (Baker, Fillmore, & Lowe, 1998; Fillmore, Wooters, & Baker, 2001). They form an intermediate representational layer between the natural language input and the representations that can be used in qualitative reasoning, e.g. model fragments in CML (Falkenhainer et al., 1994). Introduction When people read descriptions of physical phenomena in textbooks they usually have certain expectations about the information and mentally construct appropriate models of the described phenomena. This construction is an idiosyncratic process, because the readers need to interpret the author’s description, building their own model of it. Readers have to use their background knowledge to eliminate potentially ambiguous interpretations and to fill gaps left by the natural language description. In other words, reading about physical processes involves interpreting the text by constructing a model. In the best case, it is an exact reconstruction of the author’s intended model of the process. QP theory (Forbus, 1984) concerns the structure of a class of physical theories, and has been successfully used in a variety of reasoning systems (Forbus, 1996). The hypothesis is that many mental models of physical phenomena can be expressed in this formalism. QP theory has been used to develop a wide range of models of phenomena, including economics, ecology and medicine in addition to physical models. This makes it an excellent candidate for a component in a larger system of natural language semantics. In this paper we describe a natural language system that captures QP-relevant information from descriptions of instances of physical processes. It uses a deep semantic interpretation process and represents the contents as a set of interconnected QP frame structures. The system combines the results of our previous work, it is fully implemented, and has been tested on a dozen paragraphsized natural language descriptions (Kuehne, 2004). We begin with an overview of the system and its individual components. A detailed example illustrates the interpretation process, followed by a comparison of the information captured in terms of QP frame structures with a manually constructed process model. Finally, we discuss a number of problems that we encountered in using the system to capture QP-relevant information from natural language text and plans for future work. The fact that humans can learn about the physical world from textbooks and other sources leads to a number of interesting questions about the connections between our conceptual understanding of the physical world and how it is reflected in natural language. If students can learn from simple descriptions of physical phenomena, can the knowledge included in these texts be extracted to automatically construct models of the underlying physical processes?