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	title        = {Learning pressures reduce morphological complexity: Linking corpus, computational and experimental evidence.},
	abstract     = {The morphological complexity of languages differs widely and changes over time. Pathways of
change are often driven by the interplay of multiple competing factors, and are hard to disentangle. We here focus on a paradigmatic scenario of language change: the reduction of morphological complexity from Latin towards the Romance languages. To establish a causal explanation for
this phenomenon, we employ three lines of evidence: 1) analyses of parallel corpora to measure
the complexity of words in actual language production, 2) applications of NLP tools to further
tease apart the contribution of inflectional morphology to word complexity, and 3) experimental
data from artificial language learning, which illustrate the learning pressures at play when morphology simplifies. These three lines of evidence converge to show that pressures associated with
imperfect language learning are good candidates to causally explain the reduction in morphological complexity in the Latin-to-Romance scenario. More generally, we argue that combining
corpus, computational and experimental evidence is the way forward in historical linguistics and
linguistic typology.},
	booktitle    = {Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC at COLING 2016): 222–232},
	author       = {Bentz, Christian and Berdicevskis, Aleksandrs},
	year         = {2016},
	publisher    = {Association for Computational Linguistics},

	title        = {Automatic parsing as an efficient pre-annotation tool for historical texts},
	abstract     = {Historical treebanks tend to be manually annotated, which is not surprising, since state-of-the-art parsers are not accurate enough to ensure high-quality annotation for historical texts. We test whether automatic parsing can be an efficient pre-annotation tool for Old East Slavic texts. We use the TOROT treebank from the PROIEL treebank family. We convert the PROIEL format to the CONLL format and use MaltParser to create syntactic pre-annotation. Using the most conservative evaluation method, which takes into account PROIEL-specific features, MaltParser by itself yields 0.845 unlabelled attachment score, 0.779 labelled attachment score and 0.741 secondary dependency accuracy (note, though, that the test set comes from a relatively simple genre and contains rather short sentences). Experiments with human annotators show that preparsing, if limited to sentences where no changes to word or sentence boundaries are required, increases their annotation rate. For experienced annotators, the speed gain varies from 5.80% to 16.57%, for inexperienced annotators from 14.61% to 32.17% (using conservative estimates). There are no strong reliable differences in the annotation accuracy, which means that there is no reason to suspect that using preparsing might lower the final annotation quality.},
	booktitle    = {Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities (LT4DH at COLING 2016): 62–70.},
	author       = {Eckhoff, Hanne and Berdicevskis, Aleksandrs},
	year         = {2016},
	publisher    = {Association for Computational Linguistics},

	title        = {From diachronic treebank to dictionary resource: the Varangian Rus' project},
	booktitle    = {Proceedings of the EURALEX 2016 conference: 335–340},
	author       = {Eckhoff, Hanne and Berdicevskis, Aleksandrs},
	year         = {2016},
	publisher    = {Ivane Javakhishvili Tbilisi State University},

	title        = {The beginning of a beautiful friendship: rule-based and statistical analysis of Middle Russian},
	booktitle    = {Computational linguistics and intellectual technologies. Papers from the annual international conference "Dialogue", 15: 99–111},
	author       = {Berdicevskis, Aleksandrs and Eckhoff, Hanne and Gavrilova, Tatjana},
	year         = {2016},
	publisher    = {Russian State University for the Humanities},

	title        = {Redundant features are less likely to survive: empirical evidence from the Slavic languages},
	abstract     = {We test whether the functionality (non-redundancy) of morphological features can serve as a predictor of the survivability of those features in the course of language change. We apply a recently proposed method of measuring functionality of a feature by estimating its importance for the performance of an automatic parser to the Slavic language group. We find that the functionality of a Common Slavic grammeme, together with the functionality of its category, is a significant predictor of its survivability in modern Slavic languages. The least functional grammemes within the most functional categories
are most likely to die out.},
	booktitle    = {The Evolution of Language: Proceedings of the 11th International Conference (EVOLANG11), 69–77},
	author       = {Berdicevskis, Aleksandrs and Eckhoff, Hanne},
	year         = {2016},
	ISBN         = {978-1-326-61450-8},