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<Paper uid="C88-1065">
  <Title>Exploiting Lexical Regularities in Designing Natural Language Systems</Title>
  <Section position="8" start_page="521" end_page="521" type="concl">
    <SectionTitle>
7. Lexical Acquisition
</SectionTitle>
    <Paragraph position="0"> A natural language system must be built in such a way that is is easy to expande its coverage, not only by increasing the size of the lexicon but also by adding to the set of different phenomena covered by its grammar. Due to the large number of semantic-syntactic interdependencies, increasing the coverage of a system's grammar might seem to be prohibitively expensive.</Paragraph>
    <Paragraph position="1"> It would require meddling with the entries of every verb in the Lexicon, in order to register its behavior with respect to the new phenomenon. But once a phenomenon is assoeiated with one or more verb classes, it need only be associated with these classes.</Paragraph>
    <Paragraph position="2"> There is no need to tamper with the entries of the individual verbs or construct verb-specific S-rules, as long as the lexical entries of verbs indicate class membership. Thus the problem of incorporating new phenomena is considerably simplified.</Paragraph>
    <Paragraph position="3"> The process of lexical acquisition (adding new words to the Lexicon and specifying the relevant information about them) i,-~ very simple in START. Introducing a new lexical item amounts to little more than appending it to a list of similar words, adding a few idiosyncratic features when necessary. For example, if we wanted to add the verb annoy to START's lexicon, we would simply have to add the verb together with an indication that it is a member of the emotional-reaction class.</Paragraph>
    <Paragraph position="4"> (1O0) (annoy :verb :emotional-reactlon) The lexical entry would not need to contain an explicit indication that this verb participates in the property-factoring alternation since the S-rule representing this alternation makes explicit that this property holds of all members of the emotional-reaction clw~s. The class mcntbershiI) indication in annoy's lexical entry would allow the S-rules that apply to the emotional-reaction cla:~s in general to apply to this verb in partienlar, so that STAI~C will be able t(, hm~(lle sequences such as the following: null (10\]) Inpu*: '_/'he dog annoyed the guests with its loud barking.</Paragraph>
    <Paragraph position="5"> (2ueaiion: Whom did the dog's lond harking annoy? S!/'A R.T: q?he dog's loud barking annoyed the guests.</Paragraph>
    <Paragraph position="6"> The acquisition of S-rules is equally simple in the STAHT system due to a special component ~ha.t allows STAI-~I' to in~eer S..rules tram examples. Adding a new S-rule to the system requires typing in a set of English sentences which capture a specific insta,me of the rule. lh)r insl;ance, a pair of declar~tive seats, tees (sa,:h as (35) and (36)), which exemplify the property-f~w.toring alternation, can be used by S'\]'ARSi? to i~ffer the celated S rule. '.I.'o do t, his, STAtG.' analyzes the sentences, queries th,~ user for ad(titional information regarding elements of co~responding T-expre'~sions (a,';certaining whether they are too.lashing variable, Z constants, or predicates), and then builds trod general\zes the S--rule automatically.</Paragraph>
    <Paragraph position="7"> Carefld examination of English verb cbmses (see Levin \[to appear\]) combined with the effective employment of S-rules allows the system to red*me to a minimuln the amount of idiosyncoat, is ,~ynt~etie and semantic information in the Lexicon. All this makes t,he system transportable; that is, it is easily adaptable to new domains.</Paragraph>
    <Paragraph position="8"> go Coneiasion The addition of a componen~ that explicitly encodes verb classes and their characteristic properties, enables the START system to handle s wide range of phenomena reflecting sernantic-synt~etlc correspondences that are characteristic of English verbs. By t?~(:toring properties that, belong to whole classes of verl:,s out of the entries of individual verbs and letting these entries simply designate the verb's class membership, we do more than merely simplify entries. We facilitate the addition of new words to the lexicon and make it easier to extend tile system's coverage of linguistic phenomena.</Paragraph>
    <Paragraph position="9"> A(.'knowledgn-mnd;s We arc gral;e.ful to aa~e Simpson, Misha Katz, and Tom Maril\] for bdpfnl comme_u{s mtd suggestions concerning this paper, and to Jeff Pah,mcci who rontributed signiiieantly to the Question-Answering pa~rt of the system.</Paragraph>
    <Paragraph position="10"> This paper describes research done at the Massachusetts Institute of Technology. Support tbr i(atz's work was provided in part by the Advanced P~esearch Projects Agency under Offlee of Naval Resc~rch contract N0014-85-K-0124. Support for Levin~s work w~m provided in part by a grant to the Lexicon Project of the MIT Center for Cognitive Science from the System Derek,parent Foundation.</Paragraph>
  </Section>
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