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<Paper uid="C82-2072">
  <Title>I~RAN AND PHRED: ANALYSIS AND I~ODUCTION USING A CO~0N KNOWLEDG~BASE</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 o0 Introduction
</SectionTitle>
    <Paragraph position="0"> We propose a model of language use that is derived from wlewl~ language processing systems as knowledge-based systems. The knowledge that needs to be represented and organized here is the large amount of knowledge about what the utterances of a language mean. In this paper, I describe some of the theoretleel underpinni~e of the model, and then desorlbe two programs, PHRAN and I~RED, that are based on these ideasdeg We have conducted a number of experiments with these systems that have some bearing on the utility of the model &amp;quot;s presumptions, including testing these systems on other languages (Spahish and Chinese), and implementing one of them in a relation- null Language user knows a great number of fact about what utterances of their language mean. That is, in addition to knowing the meanings of a large number of words, they know the steaL%license of a set of meaningful lingu/stto units that are not necessarily understood in term8 of thei~ components. Our conjecture is that such units constitute a very considerable fraction of the language knowledge needed by an intelligent language processor.</Paragraph>
    <Paragraph position="1">  - 296 2.2. 3harable Knowledge Base  In our model, it is assumed that the knowledge used for analysis and for production is by and laxge the same. That is, there is only one data base of knowledge about the meanings of a language's forms. By having the knowledge of the two components be a shared data base, only one form of representation is needed. Moreover, the addition of new knowledge to thAs data base extends the capabilities of both systems simultaneously.</Paragraph>
    <Paragraph position="2"> As thAs requirement forces knowledge to be represented deolaratively, the other benefits of such representations eme enjoyed as well. For exsunple, in this format, knowledge about the language is kept separate from the processing strategies that apply this knowledge to the understanding and production tasks. Thuue adding new knowledge requires only adding new asssztionB to the data base, not writing and debug~ new code. In addition, other knowledge besides the mesu~ng of a phrase can be easily associated with such declarative representations. null 3.0. PHRAN and P~h~ED We have been developing this model of language use in two related programs, PHRAN (PHRasal ANs~yzer) and PHRED (PHRasal ~ngllsh Diction). PHRAN is a language understanding program written by Y=igal Arens. It reads English sentences and produces representations from them that encode their meaning. PHRED is a natural language production meohanAs~, developed by Steven Upstill. PHRED takes meaning representations as input and expresses them i~ ~nglish sentences. Both PHRAN and PHRED shs~e a common data base of language knowledge. This data base contains declarative representations about what the phrase of the English language mean. This knowledge is stored in the form of p attex~-conoeDt pears. A pattern is a phrasal construct of varying de~rees of specificity. The concept part of a pattern-concept pair is a ogn- 297 ceptual template that represents the meaning of the associated phrase. Together, these pairs associate different forms of utterances with their meanings, PHRAN understands by reading the input text and trying to find the phrasal patterns that apply to it. Eventually, the conceptual template associated with the desired pattern is used to generate the structure denoting the meaning of the utterance. PHRED produces sentences that encode an idea by examining the same knowledge base.</Paragraph>
    <Section position="1" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
4.0 Spanish and Chinese PHRAN
</SectionTitle>
      <Paragraph position="0"> We have build both a Spanish and a Chinese version of PHRAN simply by chansing the pattern-concept data base. These programs lend support to some of the claims we make for our model. We found that it was possible to rewrite most of the patterns into phrases of another language without havinsgthe knowledge encoder learn anything about the inner workings of the program. This suggests that a system like PHR~ could be designed to allow fairly easy construction of a language processor for a new language, or to allow for the addition of special purpose phrases or Jargon by some user who was not an expert AI programmer.</Paragraph>
      <Paragraph position="1"> 5.0 AI and Relation Data Bases We implemented a version of PHRAR in a conventional data base system. PItR.kN was re-written in EQEEL, a query language for the INGRES relational data base system developed at Berkeley. Tests were run to compare the relative perforemnce of the systems on various size data bases.</Paragraph>
      <Paragraph position="2"> The results can be summarized as follows: The LISP version is considerably faster when the data base of pattern-concept pairs is small. However, when the data base is large (2000 words and 500 patterns), the EQUEL version is about 3 times faster than the LISP version. Thus performance problems in natural lan~Aag@ may be solved by importing developments in data base technology as the size of our knowledge bases grow.</Paragraph>
      <Paragraph position="3"> - 298 -</Paragraph>
    </Section>
  </Section>
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