Mnoho starších poznámek mám ve Wordu.

Yet older notes exist at UMIACS NLP Wiki.

setenv $PADAPT /net/work/people/zeman/padapt
cd $PADAPT

• Classifier combination. Merge Charniak n-best lists from gloss and delex (and acquis-gloss and acquis-delex). Either let them vote, or let the reranker select from the merged list.
• Find a way to estimate trustworthiness of parses in self-training. Charniak’s rank in n-best list? Voting of three or more parsers? Some sort of sentence classifier that would estimate how easy it is for Charniak to parse? Maybe start with short sentences only? If we can reliably say what parses by Charniak can be trusted, we can restrict self training to those and see what happens.
• Immediately follows the bootstrapping experiment we discussed earlier. It failed but maybe it would not, had we been able to distinguish good from bad examples.
• Reverse the gloss experiment. “Translate” (gloss) Danish training data to Swedish, train a parser on it, test the parser on Swedish.
• Refine glossing. Translate N (100?) most frequent words. Of the rest, translate only M (4? 5?)-letter suffixes. Suffixation is another way of delexicalization and it would also be interesting how it affects accuracy of monolingual parsing.
• Explore the learning curve of the adapted parsers, as one of the reviewers suggested. Gradually increase the amount of Danish training data, monitor the changes in Swedish parsing accuracy.
• Test other languages with different degrees of relatedness. No new research, thus low priority; on the other hand, this could give answer to the doubts one of the reviewers had.
• Test a dependency parser in similar setting. After all, the treebanks we work with are of dependency origin.
• As reviewers suggested more error analysis. What is the nature of the most frequent errors that the adapted parser does and the Danish parser does not? Are they caused by lexical divergences? Morpho-syntactic? Domain mismatch?
• Use Swedish training data instead of Acquis. Strip the structure from them but keep the gold-standard POS tags. This experiment could show the impact of tagging errors. It provides a different domain, too.