# A system for information extraction from scientific texts in Russian

arXiv: https://arxiv.org/abs/2109.06703 · HTML: https://mlsanity.com/paper/2109.06703/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 14 Sep 2021.

## Builds on (most cited first, 13 of 13)

- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019)
- [TLDR: Extreme Summarization of Scientific Documents](https://mlsanity.com/paper/2004.15011/) (2020)
- [Span-based Joint Entity and Relation Extraction with Transformer Pre-training](https://mlsanity.com/paper/1909.07755/) (2019)
- [Enriching Pre-trained Language Model with Entity Information for Relation Classification](https://mlsanity.com/paper/1905.08284/) (2019)
- [Deep Joint Entity Disambiguation with Local Neural Attention](https://mlsanity.com/paper/1704.04920/) (2017)
- [Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation](https://mlsanity.com/paper/1601.01343/) (2016)
- [Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols](https://mlsanity.com/paper/2009.14237/) (2020)
- [Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation](https://mlsanity.com/paper/1504.07678/) (2015)
- [The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources](https://mlsanity.com/paper/2003.01006/) (2020)
- [Enhancing Relation Extraction Using Syntactic Indicators and Sentential Contexts](https://mlsanity.com/paper/1912.01858/) (2019)
- [Entity Recognition and Relation Extraction from Scientific and Technical Texts in Russian](https://mlsanity.com/paper/2011.09817/) (2020)
- [What if we had no Wikipedia? Domain-independent Term Extraction from a Large News Corpus](https://mlsanity.com/paper/2009.08240/) (2020)

## Abstract (arXiv)

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.