A Treebank-based Investigation of IPP-triggering Verbs in Dutch

sv: Bestämmelserna i detta avtal får ändras eller revideras
da: Bestemmelserne i denne aftale kan ændres og revideres
no: Bestemmelsene i denne avtalen kan endres eller revideres
helt eller delvis efter gemensam överenskomst mellan parterna.
helt eller delvis efter fælles overenskomst mellem parterne.
helt eller delvis etter felles overenskomst mellom partene.
en: The provisions of the Agreement may be amended and revised as a whole
or in detail by common agreement between the two Parties.
Figure 3: Swedish-Danish parallel example.
driver we have written for the Norwegian tagset. We then delexicalise the corpora,
replacing the word form and lemma columns in the CoNLL data with dummy
values, leaving only the PoS information in the treebank, and train parsers on the
delexicalised treebanks. The “Basic” column of the first two rows of Table 1 show
the performance of these parsers on the Norwegian test corpus. We tried to apply
Søgaard’s [9] filtering technique in conjunction with these experiments, but it did
not have any noticeable impact on performance, confirming Søgaard’s hypothesis
that this technique is most useful for unrelated or distantly related languages.
3.2 Lexicalised parsing
Not delexicalising the source corpora in a cross-lingual setting is unconventional,
but as noted above the Scandinavian languages are extremely similar. Figure
3, shows an example parallel sentence 4 (Zeman and Resnik [12]), and we may
note that of the 16 words in the sentence, 5 are identical between the Danish and
Swedish versions. In the Norwegian version, 9 words (i, kan, og, revideres, helt,
eller, delvis, overenskomst) are indentical to the word in the Danish version and 6
(i, får, eller [twice], helt, delvis) in the Swedish. Given this similarity, our hypothesis
was that keeping the words might give a small performance boost by making
it possible to learn some selectional preferences during parser training.
Looking more closely at the lexical overlaps between the training corpora and
our test corpus, we find that, disregarding punctuation, out of the word-forms encountered
in the Swedish training set, 338 also occur in the test corpus (a total of
3151 times), and 660 word-forms from the Danish corpus occur a total of 4273
times. This means that out of the 6809 non-punctuation tokens in the Norwegian
test corpus, 46% of them are known words to a lexicalised Swedish parser and 63%
are known to a lexicalised Danish parser.
The “Basic” column of Table 1 shows the results of our experiments with the
parsers described in this and the previous section. As the table makes clear, Talbanken
fares far better than DDT as source corpus. Given the large overlap in
4 The sentence is from the Acquis corpus. The Swedish, Danish and English versions are those in
Acquis; Norway not being a member of the EU, we have supplied our own Norwegian translation.
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