Corpus ID: 57168954

Russian natural language processing for computer-assisted language learning: Capturing the benefits of deep morphological analysis in real-life applications

@inproceedings{Reynolds2016RussianNL,
  title={Russian natural language processing for computer-assisted language learning: Capturing the benefits of deep morphological analysis in real-life applications},
  author={Robert Joshua Reynolds},
  year={2016}
}
xvii Acknowledgements xix 
Prerequisites for Shallow-Transfer Machine Translation of Mordvin Languages: Language Documentation with a Purpose
TLDR
The current lexical, morphological, syntactic and rule-based machine translation work for Erzya and Moksha that can and should be used in the development of a roadmap for Mordvin linguistic research are presented. Expand
Applying Probabilistic Tagging To Russian Poetry
TLDR
A number of probabilistic taggers based on decision trees, CRF and neural network algorithms as well as one state-of-the-art dictionary-based tagger are evaluated, focusing on the taggers’ performance in the identification of the part of speech tags and lemmas. Expand
A Cross-Genre Morphological Tagging and Lemmatization of the Russian Poetry: Distinctive Test Sets and Evaluation
TLDR
A method to compile the gold standard datasets for the Russian poetry and a focus on the taggers’ performance in the identification of the part of speech tags and lemmas is focused on. Expand
Analyzing Linguistic Complexity and Accuracy in Academic Language Development of German across Elementary and Secondary School
TLDR
It is shown that classifiers for the early years rely more on accuracy development, whereas development in secondary school is better characterized by increasingly complex language in all domains: linguistic system, language use, and human sentence processing characteristics. Expand
Using Broad Linguistic Complexity Modeling for Cross-Lingual Readability Assessment
TLDR
It is shown that the linguistic complexity analyses for the cross-language experiments identify features successfully characterizing the readability of texts for language learners across languages, as well as some language-specific characteristics of different reading levels. Expand
NEALT Proceedings Series Vol. 47 Proceedings of the 10th Workshop on Natural Language Processing for Computer Assisted Language Learning (NLP4CALL 2021)
In this paper, we present an experiment performed with the aim of evaluating if linguistic knowledge of expert quality about Romanian synonyms could be crowdsourced from L1 language learners,Expand
CTAP for Italian: Integrating Components for the Analysis of Italian into a Multilingual Linguistic Complexity Analysis Tool
TLDR
This work presents the Italian component of CTAP, describes its implementation and compares it to the existing linguistic complexity tools for Italian, currently the most comprehensive linguistic complexity measurement tool for Italian and the only one allowing the comparison of Italian texts to multiple other languages within one tool. Expand
Computationally Modeling the Impact of Task-Appropriate Language Complexity and Accuracy on Human Grading of German Essays
TLDR
This paper shows that teachers successfully assign higher language performance grades to essays with higher task-appropriate language complexity and properly separate this from content scores, based on texts written by students for the official school-leaving state examination. Expand
From lexical triggers to contextual cues : Sentence complexity and aspectual choice in Russian narrative sequences
Selecting a perfective or an imperfective verb presents a challenge for non-native speakers of Russian. Descriptive grammars list various lexical “triggers” that indicate that only one aspect isExpand
Construal vs. redundancy: Russian aspect in context
Abstract The relationship between construal and redundancy has not been previously explored empirically. Russian aspect allows speakers to construe situations as either Perfective or Imperfective,Expand
...
1
2
...

References

SHOWING 1-10 OF 92 REFERENCES
Natural Language Processing in Computer-Assisted Language Learning
This chapter examines the application of natural language processing to computerassisted language learning including the history of work in this field over the last thirtyfive years but with a focusExpand
Natural language processing and language learning
As a relatively young field of research and development that began with work on crypt-analysis and machine translation around 50 years ago, natural language processing (NLP) is concerned with theExpand
The proper place of men and machines in language technology Processing Russian without any linguistic knowledge
The paper describes several experiments aimed at designing tools for processing Russian texts, namely for Part-Of-Speech tagging, lemmatisation and syntactic parsing, exploiting exclusivelyExpand
Morphological Analyzer and Generator for Russian and Ukrainian Languages
pymorphy2 is a morphological analyzer and generator for Russian and Ukrainian languages. It uses large efficiently encoded lexicons built from OpenCorpora and LanguageTool data. A set ofExpand
Single-Sentence Readability Prediction in Russian
TLDR
This study attempts to discover and analyze a set of possible features that can be used for single-sentence readability prediction in Russian and test the influence of syntactic features on predictability of structural complexity. Expand
A preliminary constraint grammar for Russian
TLDR
This paper presents preliminary work on a constraint grammar based disambiguator for Russian that is tuned to be high recall (over 0.99) at the expense of low precision. Expand
Morphological Processing and Computer-Assisted Language Learning
TLDR
The position of NLP within CALL is discussed using GLOSSER, an intelligent assistant for Dutch students learning to read in French, which relies essentially on lemmatization, part-of-speech (POS) disambiguation, lexeme indexing, and... Expand
Hand-Crafted Rules
TLDR
As already stated in Chapter 8, a linguistic tagger can consist of the following modules: Tokenizer, Morphological analyser, and heuristic grammar(s). Expand
Automatic methods for lexical stress assignment and syllabification
Improvements in automatic lexical stress assignment and syllabification can increase the quality of text-to-speech synthesis as well as decrease the memory requirements for dictionaries. SeveralExpand
Abstract phonology in a concrete model : cognitive linguistics and the morphology-phonology interface
This book offers a welcome contribution to phonology and morphology, which have been understudied in cognitive linguistics. A detailed account of Russian verbs illustrates the efficacy of CognitiveExpand
...
1
2
3
4
5
...