<?xml version="1.0" standalone="yes"?>
<Paper uid="T75-2004">
  <Title>Woods, W.A., Kaplan, R.A., &amp; Nash-Webber, B., &amp;quot;The Lunar Sciences Natural Language Information System: Final Report: BBN</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
THE CLOWNS MICROWORLD*
</SectionTitle>
    <Paragraph position="0"/>
  </Section>
  <Section position="2" start_page="0" end_page="17" type="abstr">
    <SectionTitle>
ABSTRACT
</SectionTitle>
    <Paragraph position="0"> About fifteen years of active research in natural language question-answering systems has provided reasonably concise and elegant formulations of computational semantics far understanding English sentences and questions about various microworlds.</Paragraph>
    <Paragraph position="1"> These include the Woods Lunar Data Base, the Winograd world of a pictured hand and blocks, the Heidorn world of a fueling station, the Hendrix, Slocum, Thompson world of transactions, John Seely Brown's power circuit and Schank's sketches of motivated humans. (See Woods et al 1972, Winograd 1972, Hendrix et al 1973, Heidorn 1972, Schank 1975 and Brown et al 1974.) In each of these worlds, a natural language processor is able to understand an ordinary subset of English and use it conversationally to accept data and to respond to commands and questions.</Paragraph>
    <Paragraph position="2"> Ignoring early work largely lost in the archives of corporate memos, Wino~rad's language processor is essentially a first reporting of how to map English sentences into diagrammatic pictures. Apart from potential applications, the pictures are of great value in providing a universally understood second language to demonstrate the system's interpretation of the English input. While we are still struggling in early stages of how to compute from English descriptions or instructions, there is much to be gained from studying the subset of English that is picturable. Translation of English into other more general languages such as predicate calculus, LISP, Russian, Basic Engish, Chinese, etc. can provide the same feedback as to the system's interpretation and must suffice for the unpicturable set of English. But for teaching purposes, computing pictures from language is an excellent instrument.</Paragraph>
    <Paragraph position="3"> We began with the notion that it should be quite easy to construct a microworld concerning a clown, a pedestal and a pole. The resulting system* could draw pictures for such sentences as: A clown holding a pole balances on his head in a boat.</Paragraph>
    <Paragraph position="4"> A clown on his arm on a pedestal balances a small clown on his head.</Paragraph>
    <Paragraph position="5"> Figure I shows examples of diagrams produced in response to these sentences.</Paragraph>
    <Paragraph position="6">  We progressed then to sentences concerning movement by adding land, water, a lighthouse, a dock and a boat. We were then able to draw pictures such as Figure 2 to represent the meanings of: A clown on his head sails a boat from the dock to the lighthouse.</Paragraph>
    <Paragraph position="7"> In the context of graphics, two dimensional line drawings are attractive in their simplicity of computation. An object is defined as a LOGO graphics program that draws it (see Papert 1971). A scene is a set of objects related in terms of contact points. A scene can be described by a set of predicates:  Orientation functions for adjusting starting points and headings of the programs that draw the objects are required and these imply some trigonometric functions. A LISP package of about 650 lines has been developed by Gordon Bennett-Novak to provide the picture making capability.</Paragraph>
    <Paragraph position="8"> What is mainly relevant to the computation of language meanings is that a semantic structure sufficient to transmit data to the drawing package is easily represented as a property list associated with an artificial name for the scene. For example, &amp;quot;A CLOWN ON A PEDESTAL&amp;quot; results in the following structure:  (CI, TOK CLOWN, SUPPORTBY C2, ATTACH(CI FEETXY C2 TOPXY)) (C2, TOK PEDESTAL, SUPPORT CI, ATTACH(C2 TOPXY CI FEETXY)) (CLOWN, EXPR(LAMBDA()...) FEET XY, SIZE 3, STARTPT XY, HEADING A) (PEDESTAL, EXPR(LAMBDA()...) TOP XY, SIZE 3, STARTPT XY, HEADING A)  A larger scene has more objects, more attach relations, and may include additional relations such as INSIDE, LEFTOF, RIGHTOF, etc. In any case the scene is semantically represented as a set of objects connected by relations in a graph (i.e. a semantic network) that can easily be stored as objects on a property list with relational attributes that connect them to other such objects. A small grammar rich in embedding capabilities is coded in Woods&amp;quot; form of Augmented Transition Net (Woods 1970) for a set of ATN functions to interpret. As each constituent is completed the operations under the grammar arcs create portions of property list structure. When a clause is completed, semantic routines associated with verbs and prepositions sort the various Subject Object and Complement constituents into semantic roles and connect them by semantic relations. A verb of motion creates a net of relations that are valid in all timeframes and in addition encodes a process model that changes the semantic net from one timeframe to another.</Paragraph>
    <Paragraph position="9"> Nouns such as &amp;quot;clown&amp;quot;, &amp;quot;lighthouse&amp;quot;, &amp;quot;water&amp;quot;, etc. are programs that construct images on a display screen. Other nouns such as &amp;quot;top&amp;quot;, &amp;quot;edge&amp;quot;, &amp;quot;side&amp;quot; etc are defined as functions that return contact points for the pictures. Adjectives and adverbs provide data on size and angles of support. Prepositions and verbs are defined as semantic functions that explicate spatial relations among noun images. Generally, a verb produces a process model that encodes a series of scenes that represent initial, intermediate and final displays of the changes the verb describes.</Paragraph>
    <Paragraph position="10"> The system is programmed in UTLISP for CDC equipment and uses an IMLAC display system. It currently occupies 32K words of core and requires less than a second to translate a sentence into a picture.</Paragraph>
  </Section>
class="xml-element"></Paper>