Introducing baselines for Russian named entity recognition

Current research efforts in Named Entity Recognition deal mostly with the English language. Even though the interest in multi-language Information Extraction is growing, there are only few works reporting results for the Russian language. This paper introduces quality baselines for the Russian NER task. We propose a corpus which was manually annotated with organization and person names. The main purpose of this corpus is to provide gold standard for evaluation. We implemented and evaluated two approaches to NER: knowledge-based and statistical. The first one comprises several components: dictionary matching, pattern matching and rule-based search of lexical representations of entity names within a document. We assembled a set of linguistic resources and evaluated their impact on performance. For the data-driven approach we utilized our implementation of a linear-chain CRF which uses a rich set of features. The performance of both systems is promising (62.17% and 75.05% F1 measure), although they do not employ morphological or syntactical analysis. © 2013 Springer-Verlag

RuLegalNER: a new dataset for Russian legal named entities recognition

We address the scarcity of datasets specifically tailored for legal NER in the Russian language and investigate the generalization capabilities of models towards unseen named entities. A rule-based program developed by legal experts at Tag-Consulting Company was employed to automatically annotate legal texts and create the RuLegalNER dataset. Part of the named entities only exists in the development and test splits, and they are unseen in the training set. RuBERT was utilized as the base architecture for experimental evaluation. Two different architectural extensions were explored: RuBERT with CRF and RuBERT with adapters. These architectures were used to train and evaluate NER models on the RuLegalNER dataset. Utilize RuLegalNER to train and evaluate legal NER models, enhancing performance in the legal domain and studying generalization on unseen entities. A published version of RuLegalNER is presented with detailed statistics and demonstration of the usefulness of RuLegalNER by evaluating modern architectures

Identifying disease-related expressions in reviews using conditional random fields

As the as the volume of user-generated content in social media expands so do the potential benefits of mining social media to learn about patient conditions, drug indications, and beneficial or adverse drug reactions. In this paper, we apply Conditional Random Fields (CRF) model for extracting expressions related to diseases from patient comments. Our method utilizes hand-crafted features including contextual features, dictionaries, clusterbased and distributed word representation generated from unlabeled user posts in social media. We compare our CRF-based approach with deep recurrent neural networks and a dictionary-based approach. We examine different word embeddings generated from unlabeled user posts in social media and scientific literature. We show that CRF outperformed other methods and achieved the F1-measures of 69.1% and 79.4% on recognition of disease-related expressions in the exact and partial matching exercises, respectively. Qualitative evaluation of disease-related expressions recognized by our feature-rich CRF-based approach demonstrates the variability of reactions from patients with different health conditions

Combination of Deep Recurrent Neural Networks and Conditional Random Fields for Extracting Adverse Drug Reactions from User Reviews

© 2017 Elena Tutubalina and Sergey Nikolenko. Adverse drug reactions (ADRs) are an essential part of the analysis of drug use, measuring drug use benefits, and making policy decisions. Traditional channels for identifying ADRs are reliable but very slow and only produce a small amount of data. Text reviews, either on specialized web sites or in general-purpose social networks, may lead to a data source of unprecedented size, but identifying ADRs in free-form text is a challenging natural language processing problem. In this work, we propose a novel model for this problem, uniting recurrent neural architectures and conditional random fields. We evaluate our model with a comprehensive experimental study, showing improvements over state-of-the-art methods of ADR extraction

Geospatial data analysis in Russia’s geoweb

The chapter examines the role of geospatial data in Russia’s online ecosystem. Facilitated by the rise of geographic information systems and user-generated content, the distribution of geospatial data has blurred the line between physical spaces and their virtual representations. The chapter discusses different sources of these data available for Digital Russian Studies (e.g., social data and crowdsourced databases) together with the novel techniques for extracting geolocation from various data formats (e.g., textual documents and images). It also scrutinizes different ways of using these data, varying from mapping the spatial distribution of social and political phenomena to investigating the use of geotag data for cultural practices’ digitization to exploring the use of geoweb for narrating individual and collective identities online

Knowledge-Driven Event Extraction in Russian: Corpus-Based Linguistic Resources

Automatic event extraction form text is an important step in knowledge acquisition and knowledge base population. Manual work in development of extraction system is indispensable either in corpus annotation or in vocabularies and pattern creation for a knowledge-based system. Recent works have been focused on adaptation of existing system (for extraction from English texts) to new domains. Event extraction in other languages was not studied due to the lack of resources and algorithms necessary for natural language processing. In this paper we define a set of linguistic resources that are necessary in development of a knowledge-based event extraction system in Russian: a vocabulary of subordination models, a vocabulary of event triggers, and a vocabulary of Frame Elements that are basic building blocks for semantic patterns. We propose a set of methods for creation of such vocabularies in Russian and other languages using Google Books NGram Corpus. The methods are evaluated in development of event extraction system for Russian

Introducing baselines for Russian named entity recognition

Current research efforts in Named Entity Recognition deal mostly with the English language. Even though the interest in multi-language Information Extraction is growing, there are only few works reporting results for the Russian language. This paper introduces quality baselines for the Russian NER task. We propose a corpus which was manually annotated with organization and person names. The main purpose of this corpus is to provide gold standard for evaluation. We implemented and evaluated two approaches to NER: knowledge-based and statistical. The first one comprises several components: dictionary matching, pattern matching and rule-based search of lexical representations of entity names within a document. We assembled a set of linguistic resources and evaluated their impact on performance. For the data-driven approach we utilized our implementation of a linear-chain CRF which uses a rich set of features. The performance of both systems is promising (62.17% and 75.05% F1 measure), although they do not employ morphological or syntactical analysis. © 2013 Springer-Verlag

Introducing baselines for Russian named entity recognition

Current research efforts in Named Entity Recognition deal mostly with the English language. Even though the interest in multi-language Information Extraction is growing, there are only few works reporting results for the Russian language. This paper introduces quality baselines for the Russian NER task. We propose a corpus which was manually annotated with organization and person names. The main purpose of this corpus is to provide gold standard for evaluation. We implemented and evaluated two approaches to NER: knowledge-based and statistical. The first one comprises several components: dictionary matching, pattern matching and rule-based search of lexical representations of entity names within a document. We assembled a set of linguistic resources and evaluated their impact on performance. For the data-driven approach we utilized our implementation of a linear-chain CRF which uses a rich set of features. The performance of both systems is promising (62.17% and 75.05% F1 measure), although they do not employ morphological or syntactical analysis. © 2013 Springer-Verlag

Introducing baselines for Russian named entity recognition

Current research efforts in Named Entity Recognition deal mostly with the English language. Even though the interest in multi-language Information Extraction is growing, there are only few works reporting results for the Russian language. This paper introduces quality baselines for the Russian NER task. We propose a corpus which was manually annotated with organization and person names. The main purpose of this corpus is to provide gold standard for evaluation. We implemented and evaluated two approaches to NER: knowledge-based and statistical. The first one comprises several components: dictionary matching, pattern matching and rule-based search of lexical representations of entity names within a document. We assembled a set of linguistic resources and evaluated their impact on performance. For the data-driven approach we utilized our implementation of a linear-chain CRF which uses a rich set of features. The performance of both systems is promising (62.17% and 75.05% F1 measure), although they do not employ morphological or syntactical analysis. © 2013 Springer-Verlag