Replication data for: The beginning of a beautiful friendship: rule-based and statistical analysis of Middle Russian
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'We describe and compare two tools for processing Middle Russian texts.Both tools provide lemmatization, part-of-speech and morphological annotation.One (“RNC”) was developed for annotating texts in the RussianNational Corpus and is rule-based. The other one (“TOROT”) is being usedfor annotating the eponymous corpus and is statistical. We apply the twoanalyzers to the same Middle Russian text and then compare their outputswith high-quality manual annotation. Since the analyzers use different annotationschemes and spelling principles, we have to harmonize their outputsbefore we can compare them. The comparison shows that TOROTperforms considerably better than RNC (lemmatization 69.8% vs. 47.3%,part of speech 89.5% vs. 54.2%, morphology 81.5% vs. 16.7%). If, however,we limit the evaluation set only to those tokens for' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["diachronic","inflection","Middle Russian","morphology"]} <|eot_id|>
 Here is the Description of a dataset. Description:'We describe and compare two tools for processing Middle Russian texts.Both tools provide lemmatization, part-of-speech and morphological annotation.One (“RNC”) was developed for annotating texts in the RussianNational Corpus and is rule-based. The other one (“TOROT”) is being usedfor annotating the eponymous corpus and is statistical. We apply the twoanalyzers to the same Middle Russian text and then compare their outputswith high-quality manual annotation. Since the analyzers use different annotationschemes and spelling principles, we have to harmonize their outputsbefore we can compare them. The comparison shows that TOROTperforms considerably better than RNC (lemmatization 69.8% vs. 47.3%,part of speech 89.5% vs. 54.2%, morphology 81.5% vs. 16.7%). If, however,we limit the evaluation set only to those tokens for' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
We describe and compare two tools for processing Middle Russian texts.Both tools provide lemmatization, part-of-speech and morphological annotation.One (“RNC”) was developed for annotating texts in the RussianNational Corpus and is rule-based. The other one (“TOROT”) is being usedfor annotating the eponymous corpus and is statistical. We apply the twoanalyzers to the same Middle Russian text and then compare their outputswith high-quality manual annotation. Since the analyzers use different annotationschemes and spelling principles, we have to harmonize their outputsbefore we can compare them. The comparison shows that TOROTperforms considerably better than RNC (lemmatization 69.8% vs. 47.3%,part of speech 89.5% vs. 54.2%, morphology 81.5% vs. 16.7%). If, however,we limit the evaluation set only to those tokens for
diachronic; inflection; Middle Russian; morphology