A system for information extraction from scientific texts in Russian

09/14/2021
by   Elena Bruches, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/19/2020

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

This paper is devoted to the study of methods for information extraction...
research
09/29/2022

TERMinator: A system for scientific texts processing

This paper is devoted to the extraction of entities and semantic relatio...
research
07/03/2023

Data-Driven Information Extraction and Enrichment of Molecular Profiling Data for Cancer Cell Lines

With the proliferation of research means and computational methodologies...
research
08/15/2019

Feature-Less End-to-End Nested Term Extraction

In this paper, we proposed a deep learning-based end-to-end method on th...
research
12/14/2014

Tools for Terminology Processing

Automatic terminology processing appeared 10 years ago when electronic c...
research
12/15/2021

GenIE: Generative Information Extraction

Structured and grounded representation of text is typically formalized b...
research
07/28/2022

Visual Recognition by Request

In this paper, we present a novel protocol of annotation and evaluation ...

Please sign up or login with your details

Forgot password? Click here to reset