Connectionist Modeling of Experience-based Effects in Sentence ...

4.3 RC Extraction in Mandarin
GPE
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German SRC
der den Passant trifft verspottet
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GPE
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German ORC
den der Passant trifft verspottet
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Figure 4.2: Replication of Konieczny and Ruh (2003)
main clauses are closed by the marker le (see example 21). I, however, did not use it in
the grammar to not blur the transitivity constraints since le could seem to the network
as a noun.
(21) faguan youyu le .
Ten networks were trained on ten randomly generated corpora. Three test corpora
were randomly generated containing simple transitive SVO sentences, SRCs, and ORCs,
respectively. The respective RC test corpora only contained singly embedded RCs with
transitive verbs. It was made sure that none of the test sentences appeared in the
training sets.
(22) Test set examples:
a. gongji fayanren de lvshi biaoyang lvshimen . (SRC)
b. fayanren lijie de lvshi biaoyang yinhangjia . (ORC)
The SRN’s task was to predict the next word in a sentence. For example in an SRC,
when the verb biaoyang “praise” was seen and now the noun lushi “lawyer” was activated
the target activation for the relativizer de is 0.975. The activation pattern is shown in
figure 4.3. Besides the relativizer the transitive verbs are expected to be activated. The
reason is that there is a low but existent possibility of an object modifying ORC inside
the SRC (see example 23). This is in fact the only possible continuation apart from de
following a ‘V N’ sequence. Contrary to that in the ORC following the first two words
‘N V’ all nodes apart from the EOS are activation targets because a lot of different
continuations are possible.
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