# LASIGE and UNICAGE solution to the NASA LitCoin NLP Competition

arXiv: https://arxiv.org/abs/2308.05609 · HTML: https://mlsanity.com/paper/2308.05609/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 10 Aug 2023.

## Builds on (most cited first, 6 of 6)

- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts](https://mlsanity.com/paper/1902.09476/) (2019)
- [word2vec Parameter Learning Explained](https://mlsanity.com/paper/1411.2738/) (2014)
- [Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network](https://mlsanity.com/paper/1906.04684/) (2019)
- [BiOnt: Deep Learning using Multiple Biomedical Ontologies for Relation Extraction](https://mlsanity.com/paper/2001.07139/) (2020)
- [Does Big Data Require Complex Systems? A Performance Comparison Between Spark and Unicage Shell Scripts](https://mlsanity.com/paper/2212.13647/) (2022)

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

- [HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools](https://mlsanity.com/paper/2402.12372/) (2024)

## Abstract (arXiv)

Biomedical Natural Language Processing (NLP) tends to become cumbersome for most researchers, frequently due to the amount and heterogeneity of text to be processed. To address this challenge, the industry is continuously developing highly efficient tools and creating more flexible engineering solutions. This work presents the integration between industry data engineering solutions for efficient data processing and academic systems developed for Named Entity Recognition (LasigeUnicageNER) and Relation Extraction (BiOnt). Our design reflects an integration of those components with external knowledge in the form of additional training data from other datasets and biomedical ontologies. We used this pipeline in the 2022 LitCoin NLP Challenge, where our team LasigeUnicage was awarded the 7th Prize out of approximately 200 participating teams, reflecting a successful collaboration between the academia (LASIGE) and the industry (Unicage). The software supporting this work is available at urlhttps://github.com/lasigeBioTM/Litcoin-LasigeUnicage.