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<Paper uid="C92-1044">
  <Title>An Acquisition Model for both Choosing and Resolving Anaphora in Conjoined Mandarin Chinese Sentences</Title>
  <Section position="7" start_page="0" end_page="0" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> We have illustrated a way of using machine learning techniques to acquire anaphoric regularity in conjoined Mandarin Chinese sentences. The regularity was used to both choose and resolve anaphora with considerable accuracy. Table 4 shows a comparison between different approaches.</Paragraph>
    <Paragraph position="1"> In comparison to other approaches, tire proposal of using G-UNIMEM as the acquisition model and using semantic roles as dominant features is practical and serves multiple purposes.</Paragraph>
    <Paragraph position="2">  I .... wneos_e \] zero--5-~ro~ n~onoun~rouou~-~~_nom,nal j  I Not permitted \[ nominal \] zero \[ ~- ~----ze-m \] no~. u j~~J. c~-#&amp;Tgf&amp;__ ~ ~ i~pre~rre~ g87 T~m:es and efe e ces ~ . feature ~ notation .... semantic antecedent ante CASE)~  input: The current node N of the concept hierarchy. The name I of an unclassified instance.</Paragraph>
    <Paragraph position="3"> The set of I's unaccounted features F.</Paragraph>
    <Paragraph position="4"> Results: The concept hierarchy that classifies the instance. Top-level call: classifier( Top-node, I, F) Variables: N, N', C and NC are nodes in the hierarchy. G, H, and K are sets of features.</Paragraph>
    <Paragraph position="5"> J is an instance stored on a node.</Paragraph>
    <Paragraph position="6"> P~ is a variable of set.</Paragraph>
    <Paragraph position="7"> Classifier(N, 1, F).</Paragraph>
    <Paragraph position="8"> Let G be the set of features stores in N.</Paragraph>
    <Paragraph position="9"> Let H be the features in F that match features in G. Let K1 be the features in F that do not match features in G. Let K2 be the features in G that do not match features in H. Let H', KI' and K2' be the sets of features after Adjust(H,K1,Ki,H',KI',Ki') /* adjust goal and cause features for g-c-hierarchy or c-g-hierarchy */ if N is not the root node, then if H is empty set/* no features match */ then return False else if both H' and KI' are not empty sets then ~/* split node N */ spht N into N' and NC where NC is a child of N'; N' contains features in H' with confidence scores and I as a instance with features KI'; each confidence score in tl' is increased by 1; the remaining features and instances belong to NC; return Split. ) else if It' and H are equal/* all features match */ then increase each confidence score in N by 1. for each child C of node N/* continue match remaining features */ call Classifier(C, I, KI') and collect returns to the set It, if any Classifier(C, I, KI') call return True or Split then break. if 1% is \[False \] /* All trials fail, try to do generalization */ then for each instance J of node N call Generalize(N, J, I, KI') and collect returns to the set 1%, if any Generallze(N, J, I, KI') call return True then break. if tt is \[ False \]/* All trials fail, insert I as an instance of N */ then store I as an instance of node N with features KI'. return True.</Paragraph>
    <Paragraph position="10"> Appendix B. Sample rtllen of regularity ~ith high probability of appearance in Horn-like clauses \[an~;e (agent), gype(nil)\] :- \[anaphor(agent) ,f2(theme) ,ft (agent)\] \[ante(agent) ,type(nil)\] :- \[anaphor (agent) ,f l(agent)\] \[ante(theme) ,type(nil)\] :- \[p(bv). s2 (sub) ,d(nondefinite), anaphor (agent), f I (agent), f2 (theme)\] \[ante(theme). type(nil)\] :- \[h(nonhm), anaphor (theme). f i (agent), fi(theme)\] \[ante(theme) .type(nil)\] :- \[anaphor (theme) ,fl (theme)\] \[ante(ar E) .type(pronoun)\] :- \[anaphor(agent), f2(pred) , fl(arg)\] \[ant * (agent). type (pronoun) \] : - \[h(hm). d(definite), con(s) 0anaphor(agent). f2 (theme), f i (agent)\] \[unt e (art) ,type(pronoun)\] :- \[k(hm), anaphor (a~ent), f 2(pred) ,f l(arg)\] .. \[ante(theme). type(pronoun)\] :- \[s2(obj ) ,p(pv) ,d(definite),anaphor(theme) ,f I (agent) ,f2(theme)J \[ant e (art) .type(pronoun)\] :- \[h(hm), anaphor (theme). f2 (pred) ,f I (arg)\] Acrf~s DE COLING-92, NAN'IT.S, 23-28 AoU'r 1992 2 8 0 PROC. OF COLING-92, NANTES. AUG. 23-28, 1992</Paragraph>
  </Section>
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