CLRG ChemNER: A Chemical Named Entity Recognizer @ ChEMU CLEF 2020 Malarkodi C.S., Pattabhi, RK Rao., and Sobha, Lalitha Devi Computational Linguistics Research Group, AU-KBC Research Centre, MIT Campus of Anna University, Chennai, India sobha@au-kbc.org Abstract. This paper describes our system developed for ChEMU @ CLEF Cheminformatics Elsevier Melbourne University lab, Named En- tity Recognition (NER) task for identifying chemical compounds as well as their types in context, i.e., to assign the label of a chemical compound according to the role which the compound plays within a chemical reac- tion from patent documents. We have presented two systems which use Conditional random fields (CRFs) algorithms and Artificial Neural Net- works (ANN). In this work we used feature set that includes linguistic, orthographical and lexical clue features. In the development of systems, we have used only the training data provided by the track organizers and no other external resources or embedding models were used. We obtained an F-score of 0.6640 using CRFs and F-Score of 0.3764 using ANN on the test data. Keywords: Chemical named entity recognition, Artificial Neural Net- works, Conditional random fields, · Patent Documents. 1 Introduction CLEF 2020 ChEMU NER task aims to automatically identify chemical com- pounds and their specific types, i.e. to assign the label of a chemical compound according to the role it plays in a chemical reaction. In addition to chemical com- pounds, the task also requires identification of the temperature and the reaction time at which the chemical reaction was carried out, the yields obtained for the final chemical product and the label of the reaction. The focuses of this task is mainly on information extraction from chemical patents. This is a challenging task as patents are written very differently as compared to scientific literature. When writing scientific papers, authors strive to make their words as clear and straightforward as possible, whereas patent authors often seek to protect their knowledge from being fully disclosed [8]. Thus the main challenge for Natural Language Processing (NLP) in patent documents arises from its writing style, Copyright c  2020 for this paper by its authors. Use permitted under Creative Com- mons License Attribution 4.0 International (CC BY 4.0). CLEF 2020, 22-25 Septem- ber 2020, Thessaloniki, Greece.