Automated Spelling Correction for Clinical Text Mining in Russian

俄语诗歌文本中的拼写与语法异常检测

Detecting Spelling and Grammatical Anomalies in Russian Poetry Texts

本文针对俄语诗歌生成中训练文本质量低的问题，提出用自动化语言异常检测过滤低质量数据，比较了无监督与有监督方法，并发布人工标注的RUPOR数据集及评估代码。

Traditional Machine Learning and Deep Learning Models for Argumentation Mining in Russian Texts

Argumentation mining is a field of computational linguistics that is devoted to extracting from texts and classifying arguments and relations between them, as well as constructing an argumentative structure. A significant obstacle to research in this area for the Russian language is the lack of annotated Russian-language text corpora. This article explores the possibility of improving the quality of argumentation mining using the extension of the Russian-language version of the Argumentative Microtext Corpus (ArgMicro) based on the machine translation of the Persuasive Essays Corpus (PersEssay…

RuBioRoBERTa: a pre-trained biomedical language model for Russian language biomedical text mining

This paper presents several BERT-based models for Russian language biomedical text mining (RuBioBERT, RuBioRoBERTa). The models are pre-trained on a corpus of freely available texts in the Russian biomedical domain. With this pre-training, our models demonstrate state-of-the-art results on RuMedBench - Russian medical language understanding benchmark that covers a diverse set of tasks, including text classification, question answering, natural language inference, and named entity recognition.

一种通过跨多领域和语言的自然拼写错误模拟进行生成式拼写纠错的方法论

A Methodology for Generative Spelling Correction via Natural Spelling Errors Emulation across Multiple Domains and Languages

本文提出一种生成式拼写纠错方法，通过模拟自然拼写错误丰富模型预训练，并推出包含预训练模型和增强算法的SAGE库。

Spelling Correction with Denoising Transformer

We present a novel method of performing spelling correction on short input strings, such as search queries or individual words. At its core lies a procedure for generating artificial typos which closely follow the error patterns manifested by humans. This procedure is used to train the production spelling correction model based on a transformer architecture. This model is currently served in the HubSpot product search. We show that our approach to typo generation is superior to the widespread practice of adding noise, which ignores human patterns. We also demonstrate how our approach may be ex…

Negation Detection for Clinical Text Mining in Russian

Developing predictive modeling in medicine requires additional features from unstructured clinical texts. In Russia, there are no instruments for natural language processing to cope with problems of medical records. This paper is devoted to a module of negation detection. The corpus-free machine learning method is based on gradient boosting classifier is used to detect whether a disease is denied, not mentioned or presented in the text. The detector classifies negations for five diseases and shows average F-score from 0.81 to 0.93. The benefits of negation detection have been demonstrated by p…