Entity Recognition and Relation Extraction from Scientific and Technical Texts in Russian

Bruches, Elena; Pauls, Alexey; Batura, Tatiana; Isachenko, Vladimir

doi:10.1109/s.a.i.ence50533.2020.9303196

Cited by 13 publications

(10 citation statements)

References 11 publications

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“…It is a bit complicated to find the most suitable dataset for our purposes and in the same time open-sourced. Nevertheless, the corpus of scientific papers in Russian RuSERRC [3] solves this problem to some extent. It contains abstracts of 1.680 scientific papers on information technology in Russian, including 80 manually labeled texts with terms and relations.…”

Section: Data Descriptionmentioning

confidence: 99%

A system for information extraction from scientific texts in Russian

Bruches¹,

Mezentseva²,

Batura³

2021

Preprint

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In this paper, we present a system for information extraction from scientific texts in the Russian language. The system performs several tasks in an end-to-end manner: term recognition, extraction of relations between terms, and term linking with entities from the knowledge base. These tasks are extremely important for information retrieval, recommendation systems, and classification. The advantage of the implemented methods is that the system does not require a large amount of labeled data, which saves time and effort for data labeling and therefore can be applied in low-and mid-resource settings. The source code is publicly available and can be used for different research purposes.

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Section: Data Descriptionmentioning

confidence: 99%

A system for information extraction from scientific texts in Russian

Bruches¹,

Mezentseva²,

Batura³

2021

Preprint

Self Cite

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“…We picked the dataset RuSERRC described in Bruches et al [43]. This dataset is a collection of abstracts to scientific papers in information technologies with 1600 annotated documents and 80 manually annotated with semantic relationships: CAUSE (X yields Y), COMPARE (X is compared to Y), ISA (X is a Y), PARTOF (X is a part of Y), SYNONYMS (X is a synonym of Y), USAGE (X is used for Y).…”

Section: Semantic Relationships Extractionmentioning

confidence: 99%

“…For performance evaluation, we calculate the F-score (Table 3). In this work, we experimented with a neural network architecture from Bruches et al [43]. This neural network has four layers.…”

Section: Examples Of Rules Termsmentioning

confidence: 99%

Virtual Dialogue Assistant for Remote Exams

et al. 2021

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A Virtual Dialogue Assistant (VDA) is an automated system intended to provide support for conducting tests and examinations in the context of distant education platforms. Online Distance Learning (ODL) has proven to be a critical part of education systems across the world, particularly during the COVID-19 pandemic. While the core components of ODL are sufficiently researched and developed to become mainstream, there is still a demand for various aspects of traditional classroom learning to be implemented or improved to match the expectations for modern ODL systems. In this work, we take a look at the evaluation of students’ performance. Various forms of testing are often present in ODL systems; however, modern Natural Language Processing (NLP) techniques provide new opportunities to improve this aspect of ODL. In this paper, we present an overview of VDA intended for integration with online education platforms to enhance the process of evaluation of students’ performance. We propose an architecture of such a system, review challenges and solutions for building it, and present examples of solutions for several NLP problems and ways to integrate them into the system. The principal challenge for ODL is accessibility; therefore, proposing an enhancement for ODL systems, we formulate the problem from the point of view of a user interacting with it. In conclusion, we affirm that relying on the advancements in NLP and Machine Learning, the approach we suggest can provide an enhanced experience of evaluation of students’ performance for modern ODL platforms.

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“…Для русского языка существует корпус научных статей RuSERRC, в котором размечены термины и отношения между ними [29]. Мы дополнили этот корпус разметкой -связали выделенные термины с сущностями в Викиданных.…”

Section: описание разметкиunclassified

Automatic Linking of Terms from Scientific Texts with Knowledge Base Entities

Mezentseva

Bruches

Batura

2021

jour

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Due to the growth of the number of scientific publications, the tasks related to scientific article processing become more actual. Such texts have a special structure, lexical and semantic content that should be taken into account while processing. Using information from knowledge bases can significantly improve the quality of text processing systems. This paper is dedicated to the entity linking task for scientific articles in Russian, where we consider scientific terms as entities. During our work, we annotated a corpus with scientific texts, where each term was linked with an entity from a knowledge base. Also, we implemented an algorithm for entity linking and evaluated it on the corpus. The algorithm consists of two stages: candidate generation for an input term and ranking this set of candidates to choose the best match. We used string matching of an input term and an entity in a knowledge base to generate a set of candidates. To rank the candidates and choose the most relevant entity for a term, information about the number of links to other entities within the knowledge base and to other sites is used. We analyzed the obtained results and proposed possible ways to improve the quality of the algorithm, for example, using information about the context and a knowledge base structure. The annotated corpus is publicly available and can be useful for other researchers.

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Entity Recognition and Relation Extraction from Scientific and Technical Texts in Russian

Cited by 13 publications

References 11 publications

A system for information extraction from scientific texts in Russian

A system for information extraction from scientific texts in Russian

Virtual Dialogue Assistant for Remote Exams

Automatic Linking of Terms from Scientific Texts with Knowledge Base Entities

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