2 |
Character Alignment in Morphologically Complex Translation Sets for Related Languages ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
Composing Byte-Pair Encodings for Morphological Sequence Classification ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Variation in Universal Dependencies annotation: A token based typological case study on adpossessive constructions ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Corpus evidence for word order freezing in Russian and German ...
|
|
|
|
Abstract:
We use Universal Dependencies treebanks to test whether a well-known typological trade-off between word order freedom and richness of morphological marking of core arguments holds within individual languages. Using Russian and German treebank data, we show that the following phenomenon (sometimes dubbed word order freezing) does occur: those sentences where core arguments cannot be distinguished by morphological means (due to case syncretism or other kinds of ambiguity) have more rigid order of subject, verb and object than those where unambiguous morphological marking is present. In ambiguous clauses, word order is more often equal to the one which is default or dominant (most frequent) in the language. While Russian and German differ with respect to how exactly they mark core arguments, the effect of morphological ambiguity is significant in both languages. It is, however, small, suggesting that languages do adapt to the evolutionary pressure on communicative efficiency and avoidance of redundancy, but ...
|
|
Keyword:
Computer and Information Science; Natural Language Processing; Neural Network
|
|
URL: https://dx.doi.org/10.48448/rgn3-7512 https://underline.io/lecture/6552-corpus-evidence-for-word-order-freezing-in-russian-and-german
|
|
BASE
|
|
Hide details
|
|
7 |
Noise Isn't Always Negative: Countering Exposure Bias in Sequence-to-Sequence Inflection Models ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
Exhaustive Entity Recognition for Coptic - Challenges and Solutions ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Attentively Embracing Noise for Robust Latent Representation in BERT ...
|
|
|
|
BASE
|
|
Show details
|
|
13 |
Classifier Probes May Just Learn from Linear Context Features ...
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Seeing the world through text: Evaluating image descriptions for commonsense reasoning in machine reading comprehension ...
|
|
|
|
BASE
|
|
Show details
|
|
16 |
Manifold Learning-based Word Representation Refinement Incorporating Global and Local Information ...
|
|
|
|
BASE
|
|
Show details
|
|
17 |
HMSid and HMSid2 at PARSEME Shared Task 2020: Computational Corpus Linguistics and unseen-in-training MWEs ...
|
|
|
|
BASE
|
|
Show details
|
|
18 |
Multi-dialect Arabic BERT for Country-level Dialect Identification ...
|
|
|
|
BASE
|
|
Show details
|
|
20 |
Exploring End-to-End Differentiable Natural Logic Modeling ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|