# TERMinator: A system for scientific texts processing

arXiv: https://arxiv.org/abs/2209.14854 · HTML: https://mlsanity.com/paper/2209.14854/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 37 references that are in the map.
- First posted on arXiv 29 Sep 2022.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016)
- [Simple BERT Models for Relation Extraction and Semantic Role Labeling](https://mlsanity.com/paper/1904.05255/) (2019)
- [Span-based Joint Entity and Relation Extraction with Transformer Pre-training](https://mlsanity.com/paper/1909.07755/) (2019)
- [Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language](https://mlsanity.com/paper/1905.07213/) (2019)
- [Enriching Pre-trained Language Model with Entity Information for Relation Classification](https://mlsanity.com/paper/1905.08284/) (2019)
- [HunFlair: An Easy-to-Use Tool for State-of-the-Art Biomedical Named Entity Recognition](https://mlsanity.com/paper/2008.07347/) (2020)
- [NNE: A Dataset for Nested Named Entity Recognition in English Newswire](https://mlsanity.com/paper/1906.01359/) (2019)
- [Boundary Smoothing for Named Entity Recognition](https://mlsanity.com/paper/2204.12031/) (2022)
- [Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text](https://mlsanity.com/paper/1908.07721/) (2019)
- [NEREL: A Russian Dataset with Nested Named Entities, Relations and Events](https://mlsanity.com/paper/2108.13112/) (2021)
- [Effective Attention Modeling for Neural Relation Extraction](https://mlsanity.com/paper/1912.03832/) (2019)
- [BERT-Based Multi-Head Selection for Joint Entity-Relation Extraction](https://mlsanity.com/paper/1908.05908/) (2019)
- [Knowledge-Augmented Language Models for Cause-Effect Relation Classification](https://mlsanity.com/paper/2112.08615/) (2021)
- [Improving Relation Extraction with Knowledge-attention](https://mlsanity.com/paper/1910.02724/) (2019)
- [Enhancing Relation Extraction Using Syntactic Indicators and Sentential Contexts](https://mlsanity.com/paper/1912.01858/) (2019)
- [Relation extraction between the clinical entities based on the shortest dependency path based LSTM](https://mlsanity.com/paper/1903.09941/) (2019)
- [R-BERT-CNN: Drug-target interactions extraction from biomedical literature](https://mlsanity.com/paper/2111.00611/) (2021)

## Built on by (most cited first, 1 of 1)

- [SciMRC: Multi-perspective Scientific Machine Reading Comprehension](https://mlsanity.com/paper/2306.14149/) (2023)

## Abstract (arXiv)

This paper is devoted to the extraction of entities and semantic relations between them from scientific texts, where we consider scientific terms as entities. In this paper, we present a dataset that includes annotations for two tasks and develop a system called TERMinator for the study of the influence of language models on term recognition and comparison of different approaches for relation extraction. Experiments show that language models pre-trained on the target language are not always show the best performance. Also adding some heuristic approaches may improve the overall quality of the particular task. The developed tool and the annotated corpus are publicly available at https://github.com/iis-research-team/terminator and may be useful for other researchers.