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The results indicated that there are some differences in the use of reporting verbs between the two corpora.
As the figure shows, there is a substantial difference between the two corpora in terms of the percentage of mental and relational processes.
The figure also shows that there seems to be a divergence between the two corpora in terms of the share of the material processes.
These topics combine keywords which are typical for scientific studies in general, thus topics which can be expected to be shared between the two corpora.
The large difference in the density of phrase commands between the two corpora is explained by the fact that, in DB1, we assign one command for each intonation phrase, and in DB2, we assign one command for each pause.
More specific large topics shared between the two corpora exist as well: topics 67 (labelled "information systems") and 88 ("large-scale analysis") are examples that seem to fit data intensive research fields.
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As the multi-condition training data is generated from the clean-condition training data by adding reverberation and noise effects, we can obtain the perfect frame level alignment between the two corpus.
We found that there was minimal to no overlap between the four corpora, as shown in Table 2. Continuing on this line of comparison, we extracted all MeSH terms assigned to all documents in each corpus, and performed a comparison between them.
Results show distinct macro-structural variations across the two corpora.
By merging the two corpora, we could retain the modifications made to the BioNLP'09 corpus but gain additional events that involve protein complexes from GENIA.
The performance varied between these two corpora when considering the exact match.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com