Numerical Atrribute Extraction from Clinical Texts

This paper describes about information extraction system, which is an extension of the system developed by team Hitachi for "Disease/Disorder Template filling" task organized by ShARe/CLEF eHealth Evolution Lab 2014. In this extension module we focus on extraction of numerical attributes and values from discharge summary records and associating correct relation between attributes and values. We solve the problem in two steps. First step is extraction of numerical attributes and values, which is developed as a Named Entity Recognition (NER) model using Stanford NLP libraries. Second step is correctly associating the attributes to values, which is developed as a relation extraction module in Apache cTAKES framework. We integrated Stanford NER model as cTAKES pipeline component and used in relation extraction module. Conditional Random Field (CRF) algorithm is used for NER and Support Vector Machines (SVM) for relation extraction. For attribute value relation extraction, we observe 95% accuracy using NER alone and combined accuracy of 87% with NER and SVM.Comment: 6 Page

Transfer Learning for Scientific Data Chain Extraction in Small Chemical Corpus with BERT-CRF Model

Computational chemistry develops fast in recent years due to the rapid growth and breakthroughs in AI. Thanks for the progress in natural language processing, researchers can extract more fine-grained knowledge in publications to stimulate the development in computational chemistry. While the works and corpora in chemical entity extraction have been restricted in the biomedicine or life science field instead of the chemistry field, we build a new corpus in chemical bond field annotated for 7 types of entities: compound, solvent, method, bond, reaction, pKa and pKa value. This paper presents a novel BERT-CRF model to build scientific chemical data chains by extracting 7 chemical entities and relations from publications. And we propose a joint model to extract the entities and relations simultaneously. Experimental results on our Chemical Special Corpus demonstrate that we achieve state-of-art and competitive NER performance