Search Results for author:
Found 13 papers, 6 papers with code
Tagging and parsing of multidomain collections
In this paper we describe our submission to GramEval2020 competition on morphological tagging, lemmatization and dependency parsing.
UniMorph 3.0: Universal Morphology
The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema.
Getting More Data for Low-resource Morphological Inflection: Language Models and Data Augmentation
We investigate how to improve quality of low-resource morphological inflection without annotating more data.
LowResourceEval-2019: a shared task on morphological analysis for low-resource languages
The paper describes the results of the first shared task on morphological analysis for the languages of Russia, namely, Evenki, Karelian, Selkup, and Veps.
Tuning Multilingual Transformers for Language-Specific Named Entity Recognition
Our paper addresses the problem of multilingual named entity recognition on the material of 4 languages: Russian, Bulgarian, Czech and Polish.
Multilingual Named Entity Recognition
named-entity-recognition
+3
Convolutional neural networks for low-resource morpheme segmentation: baseline or state-of-the-art?
We apply convolutional neural networks to the task of shallow morpheme segmentation using low-resource datasets for 5 different languages.
Morphological parsing of low‑resource languages
It this paper we study morphological parsing and lemmatization on the material of Evenk and Selkup language.
Tuning Multilingual Transformers for Named Entity Recognition on Slavic Languages
Our paper addresses the problem of multilingual named entity recognition on the material of 4 languages: Russian, Bulgarian, Czech and Polish.
Multilingual Named Entity Recognition
named-entity-recognition
+3
DeepPavlov: Open-Source Library for Dialogue Systems
It supports modular as well as end-to-end approaches to implementation of conversational agents.
Improving neural morphological Tagging using Language Models
This paper addresses the task of morphological tagging and demonstrates how neural network architectures bene t from using language models for morphological tags.
Spelling Correction for Morphologically Rich Language: a Case Study of Russian
We present an algorithm for automatic correction of spelling errors on the sentence level, which uses noisy channel model and feature-based reranking of hypotheses.
