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<Paper uid="A83-1012">
  <Title>Hendrix, G. G., Sacerdoti, E. D., Sagalowicz, D., and Slocum, J., '*Developing a Natural Language Interface to Complex Data.&amp;quot; Association for Computing Machinery Transactions on Database</Title>
  <Section position="5" start_page="73" end_page="74" type="relat">
    <SectionTitle>
PREVIOUS WORK
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    <Paragraph position="0"> We would like to distinguish the KNOBS natural language facility from such familiar natural language query systems as LADDER \[Hendrix, 1978\] and LUNAR \[Woods, 1972\] in both function and method. The functional model of the above systems is that of someone with a problem to solve and a database containing information useful in its solution which he can access via a natural language interface. KNOBS, by contrast, integrates the natural language capability with multi-faceted problem solving support including critiquing and Benerating tactical plans. Our approach differs in method from these previous systems in its bottom-up, dictionary driven parsing which results in a canonical representation of the meaning of the query, its ability to perform context dependent inferences with this representation during question answering, and the use of a declarative representation of the domain to assist parsin S, question answering, plan updating, and inferencing.</Paragraph>
    <Paragraph position="1"> A system similar to APE-If in both its diccionarydriven approach to parsins and ice direct attack on word sense disambiguation is the Word Expert Parser (WEP) \[Small, 1980\]. This parser associates a discrimination net with each word to guide the meanin 8 selection process. Each word in a sentence is a pointer to a coroutine called a word expert which cooperates with neighboring words to build a meanin S representation of the sentences in a bottom-up, i.e., data driven, fashion. At each node in the discrimination net a multiple-choice test is executed which can query the lexical properties or expectations, (selectional restrictions \[Katz, 1963\]) of neighboring words, or proposed FOCUS, ACTIVITY, and DISCOURSE modules. The sense selection process of WEP requires that each word know all of the contexts in which its senses can occur. For example, to find the meaning of &amp;quot;pit&amp;quot;, the pit expert can ask if a MINING-ACTIVITY, EATING-ACTION, CAR-RACINC, or MUSIC-CONCERT-ACTION is active.</Paragraph>
    <Paragraph position="2"> APE-II evolved from APE (A Parsing Experiment), a parser used by the DSAM  is based on the CA parser \[Birnbaum, 1981\] with the addition of a word sense disambiguation algorithm. In CA, word definitions are represented as requests, a type of test-action pair. The test part of a request can check lexical and semantic features of neighboring words; the actions create or connect CD structures, and activate or deactivate other requests.</Paragraph>
    <Paragraph position="3"> The method available to select the appropriate meaning of a word in CA is to use the test part of separate requests to examine the meanings of other words and co build a meaning representation as function of this local context. For example, if the objeet of &amp;quot;serve&amp;quot; is a food, the meaning is &amp;quot;bring to&amp;quot;; if the object is a ball, the meaning is &amp;quot;hit toward&amp;quot;. This method works well for selecting a sense of a word which has expectations. However, some words have no expectations and the intended sense is the one that is expected. For example, the proper sense of &amp;quot;ball&amp;quot; in &amp;quot;John kicked the ball.&amp;quot; and &amp;quot;John attended the ball.&amp;quot; is the sense which the central action expects.</Paragraph>
    <Paragraph position="4"> The word definitions of APE are also represented as requests. A special concept called a VEL is used to represent the set of possible meanings of a word. When searching for a concept which has certain semantic features, an expectation can select one or more senses from a VEL and  discard those that are not appropriate. In addition, APE can use expectations from a contextual knowledge source such as a script applier to select a word sense. Each script is augmented with parser executable expectations called named requests. For example, aCa certain point in understanding a restaurant story, leaving * tip for the waiter is expected. The parser is then given a named request which could help disambiguate the words &amp;quot;leave&amp;quot; and &amp;quot;tip&amp;quot;, should they appear.</Paragraph>
    <Paragraph position="5"> APE-II A word definition in APE-II consists of the set of all of its senses. Each sense contains * concept, i.e., * partial CD structure which expresses the meaning of this sense, and a set of conceptual and lexical expectatious.</Paragraph>
    <Paragraph position="6"> A conceptual expectation instructs the parser to look for a concept in s certain relative position which meets a selectional restriction.</Paragraph>
    <Paragraph position="7"> The expectation also contains a selectional preference, a more specific, preferred category for the expected concept (cf. \[Wilkg, 1972\]). If such a concept is found, the expectation contains information on how it can be combined with the concept which initiated the expectation. A lexical expectation instructs the parser to look for a certain word and add a new, favored sense to it.</Paragraph>
    <Paragraph position="8"> This process is useful for predicting the function of a prepositiou \[Reisbeck, 1976\]. The definition of a pronoun utilizes a context and focus mechanism co find the set of possible referents which agree with it in number and gender. THE PRONOUN IS THEN</Paragraph>
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