Large-Scale Acquisition of LCS-Based Lexicons for Foreign Language Tutoring

Large-Scale Acquisition of LCS-Based Lexicons for Foreign Language Tutoring Bonnie J. Dorr Department of Computer Science University of Maryland College Park, MD 20742, USA bonnie©cs, urad. edu Abstract We focus on the probleln of building large repositories of le.rical coJtceplual structure (LCS) representations for verbs in multi- ple languages. One of the main results of this work is the definition of a rela- t, ion between broad semantic classes and LCS meaniug components. Our acquisi- tion program--LEXICALL--takes, as in- put, the result of previous work on verb classification and thematic grid tagging, and outputs LCS representations for differ- ent. languages. These representations have been ported into English, Arabic and Span- ish lexicons, each containing approximately 9000 verbs. We are currently using these lexicons in an operational foreign language tutoring and machine translation. 1 Introduction A wide range of new capabilities in NLP applications such as foreign language tutoring (FLT) has been made possible by recent advances in lexica.1 seman- tics (Carrier and Randall, 1993; Dowty, 1991; Fill- more, 1968; Foley and Van Valin, 1984; Grimshaw, 1990; Gruber, 1965; Hale and Keyser, 1993; Jack- endoff, 1983; aackendoff, 1990: Jackendoff, 1996; Levin, 1993; Levin and Rappaport Hovav, To ap- pear; Pesetsky, 1982; Pinker, 1989). Many of these researchers adopt the hypothesis that verbs can be grouped into broad classes, each of which corre- sponds to some combination of basic meaning con> ponents. This is the basic premise underlying our approach to multilingual lexicon construction. In particular, we have organized verbs into broad se- lnantic classes and subsequently designed a set of le,ical conceptual structures (LC, S), for each class. These representations have been ported into English, Arabic, and Spanish lexicons, each containing ap- proximately 9000 verbs. An example of a NLP application for which these lexicons are currently in use is an operational foreign language tutoring (FLT) system called Military Lan- guage Tutor (MILT). This system provides a wide range of lessons for use in language training. One of the tutoring lessons, the MicroWorld Lesson (see Figure 1) requires the capability of the language- learner to state domain-specific actions in a variety of different ways. For example, the language-learner might connnand the agent (pictured at the left in the graphical interface) to take the following action: Walt" to the table and pick up the document. The same action should be taken if the user says: Go to the table and remove document, Retrieve the docu- ment from the table, etc. The LCS representation provides the capability to execute various forms of the same command without hardcoding them as part, of the graphical interface. In another tutoring lesson, Question-Answering, the student is asked to answer questions about a foreign language text that they have read. Their answer is converted into an LCS which is matched against a prestored LCS corresponding to an answer typed in by a human instructor (henceforth, called the "author"). The prestored LCS is an idealized form of the answer to a question, which can take one of many forms. …

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