<?xml version="1.0" standalone="yes"?>
<Paper uid="C88-2162">
  <Title>Fahlman, S. E., NETL: A System for Representing and Using Real-World</Title>
  <Section position="2" start_page="0" end_page="796" type="intro">
    <SectionTitle>
1. ~TRODUCTION
</SectionTitle>
    <Paragraph position="0"> Coping with unkowns is an integral part of human communication which has been ingnored by previous linguistic models \[Chomsky81, Bresnan82, Gazdar85\]. Consider the following sentence produced by a second language speaker: John suggested her to go out, but she refused.</Paragraph>
    <Paragraph position="1"> This incorrect use of suggest could be viewed as a communication failure, since by text-book grammar suggest does not take this form of the infinitive. Alternatively, this can be viewed as a surprising success. In spite of missing lexical information, a per-son managed to convey a concept, rather than give up the communi.cation task altogether. Our aim here is to explain such robust human performance in computational terms, and consequently to describe the principles underlying the program RINA \[Zernik85, Zernik86\] which models language acquisition.</Paragraph>
    <Section position="1" start_page="0" end_page="796" type="sub_section">
      <SectionTitle>
1.1 The Modelled Behavior
</SectionTitle>
      <Paragraph position="0"> The problems arising from incomplete lexical knowledge are illustrated through the following scenario. In this scenario RINA encounters two unknown words plead*, and doove, and uses the word suggest whose lexical definition is incomplete.</Paragraph>
      <Paragraph position="1">  User: Input text: Corinne ne, eded help with her homework. Her friend Frank called and plended her to come over. But she dooved to stay home.</Paragraph>
      <Paragraph position="2"> Paraphrased text: RINA: Frank suggested her to come over.</Paragraph>
      <Paragraph position="3"> Corinne turned down the suggestion.</Paragraph>
      <Paragraph position="4"> RINA reads a paragraph provided by a user, and then generates text which conveys the state of her knowledge to the user. The first problem is presented by the word plead which does not exist in RINA's lexicon. RINA is able to extract partial information: Frank communicated a concept to Corinne regarding coming over. It is not clear, however, who comes over. Did Frank promise Corinne to come over to her, or did Frank ask Corinne to come over to him? The word doove is also unknown. Here too, RINA can guess the main concept: Corinne decided not to come over. This hypothesis is not necessarily correct. However, it fits well the context and the structure of the sentence.</Paragraph>
      <Paragraph position="5"> At this point, RINA must respond to the input text by generating a paraphrase which conveys its current hypothesis. Also in generation, RINA faces the problem of incomplete lexieal knowledge. In absence of specific knowledge regarding the use of suggest, RINA produced an incorrect sentence: he suggested her to come over, which albeit incorrect, is well understood by a human listener.</Paragraph>
    </Section>
    <Section position="2" start_page="796" end_page="796" type="sub_section">
      <SectionTitle>
1.2 The Issues
</SectionTitle>
      <Paragraph position="0"> The basic problem is this: how can any program parse a sentence when a lexical entry such as doove or plena is missing? And equivalently, how can a program use a lexical entry-suggest-which is not precisely specified? Throe knowledge sources must be negotiated in resolving this problem.</Paragraph>
      <Paragraph position="1"> * The dummy words vacua and doove are used here to bring home, even to native English speakers, the problems faced by language learners.</Paragraph>
      <Paragraph position="2"> Syntax and Control: In Frank asked Corime to come over, the word a~k actually controls the analysis of the entire sentence \[Bx'esnan82all, and detemfines the answer to the elementary question, null who comes to whom? ~l~e embedd~ phrase to come over, which does not have an explicit subject obtains its subject fi'om the control matrix \[Bresnan82a\] of ask. Accordingly, Corinne is file subject of %oming over&amp;quot;. On the other hand, in he pleaded her to come oyez, the controlliHg word plead, is yet unknown. In absence of a control matrix it is not clear how to interpret to come over. Itow can a program then, extract even partial information from text in such cinmmstances? Lex~cal Clues: Although plend itself is unknown, ThE form of rite sentence X piended Y to come over, suggests that &amp;quot;X communicated to Y a concept regarding coming over&amp;quot;. Three assumptions are implied: (a) #end is a communication act, (b) Y is the actor of &amp;quot;coming over&amp;quot;, (c) &amp;quot;coming over&amp;quot; is only a hypothetical, future act (and not an act which took place in the past). How is this intuition, which facilitates the initial hypothesis for plead, encoded in the lexicon? Contextual Clues: The hypothesis selected for doove above is a direct consequence of the context, which brings in a structure of plans and goals: (1) Corrine has an outstanding goal; (2) Frank suggests help. Given this background, the selected hypothesis is:  (3) Corinne rejects the offer. This selection is problematic since  doove could stand for other acts, e.g., she wanted to stay, she tried to stay, and she ~orgot to stay, etc. Thus, how does the context impact the selection of a hypothesis? Some of the: issues above can be handled by specific heuristic rules, custom tailored for each case. However, the challenge of this entire enterprise is to show how a unified mode! can employ its &amp;quot;normal&amp;quot; parsing mechanism in handling &amp;quot;exceptions&amp;quot;.</Paragraph>
    </Section>
    <Section position="3" start_page="796" end_page="796" type="sub_section">
      <SectionTitle>
1.3 The Hierarchical Lexicon
</SectionTitle>
      <Paragraph position="0"> Humans pelceive objects in conceptual hierarchies \[Rosch78, Fahlman79, Shapiro79, Schank82\]. This is best illustrated by an example from peoples's communication. Consider the question: what is Jack? The answer Jack is a cat is satisfactory, provided the listener knows that a cat is a mammal and a mammal is an animate. The listener need not be provided with more general facts about Jack (e.g., Jack has four logs and a tail), since such information can be accessed by inheritance from the general classes subsuming a cat. In fact, for a person who dees not know that cats are mammals, an adequate description of Jack should be more extensive. null Hierarchical organization is essential in dynamic representation systems for three reasons: o Economy: A feature shared by multiple instances should not be repetitively acquired per each instance. Such redundancy should be avoided by inheriting shared features from general classes.</Paragraph>
      <Paragraph position="1"> o Learnability: As shown by \[Winston72, MitcheU82, Kolodner84\], through a hierarchy learning can be reduced to a search process. When one acquires a new zoological term, for example feline, one can traverse the conceptual hierarchy, by generalizing and specializing, until the appropriate location is found for feline in the hierarchy-above a number of specific species, and below the general mammal.</Paragraph>
      <Paragraph position="2"> o Prediction: Hierarchy accounts for predictive power, which allows learning models to form intelligent hypotheses. When first observing a leopard and by assuming it is a feline, a learner, who has not been exposed to prior infomaation about leopards, may hypothesize that this new animal feeds, breeds, and hunts in certain ways, based on his/her knowledge of felines in general.</Paragraph>
      <Paragraph position="3"> While it is clear how hierarchy should be used in representing zoological concepts, it is not clear how it applies in representing linguistic concepts. Can linguistic systems too benefit from a hierarchical organization? Following \[Langacker86\] and \[Jacobs85\] we have shown in DHPL (Dynamic Hierarchical Phrasal Lexicon) \[Zemik88\] how elements in a lexicon can be organized in a hierarchy and thus facilitate a dynamic linguistic behavior.</Paragraph>
    </Section>
  </Section>
class="xml-element"></Paper>