WNUT-2020 Task 1 Overview: Extracting Entities and Relations from Wet Lab Protocols

This paper presents the results of the wet labinformation extraction task at WNUT 2020.This task consisted of two sub tasks- (1) anamed entity recognition task with 13 partic-ipants; and (2) a relation extraction task with2 participants. We outline the task, data an-notation process, corpus statistics, and providea high-level overview of the participating sys-tems for each sub task.

[1]  Graham Neubig,et al.  Generalizing Natural Language Analysis through Span-relation Representations , 2020, ACL.

[2]  Paul R Jaschke,et al.  Wet Lab Accelerator: A Web-Based Application Democratizing Laboratory Automation for Synthetic Biology. , 2017, ACS synthetic biology.

[3]  Doug Downey,et al.  Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks , 2020, ACL.

[4]  Anshul Wadhawan,et al.  PublishInCovid19 at WNUT 2020 Shared Task-1: Entity Recognition in Wet Lab Protocols using Structured Learning Ensemble and Contextualised Embeddings , 2020, WNUT.

[5]  Alfonso Valencia,et al.  Overview of BioCreAtIvE: critical assessment of information extraction for biology , 2005, BMC Bioinformatics.

[6]  Yiming Yang,et al.  XLNet: Generalized Autoregressive Pretraining for Language Understanding , 2019, NeurIPS.

[7]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[8]  Rajesh Ranganath,et al.  ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission , 2019, ArXiv.

[9]  Mari Ostendorf,et al.  A general framework for information extraction using dynamic span graphs , 2019, NAACL.

[10]  Ted Briscoe,et al.  Biomedical Event Extraction without Training Data , 2009, BioNLP@HLT-NAACL.

[11]  Mark A. Przybocki,et al.  The Automatic Content Extraction (ACE) Program – Tasks, Data, and Evaluation , 2004, LREC.

[12]  WNUT 2020 Shared Task-1: Conditional Random Field(CRF) based Named Entity Recognition(NER) for Wet Lab Protocols , 2020, W-NUT@EMNLP.

[13]  Jaewoo Kang,et al.  BioBERT: a pre-trained biomedical language representation model for biomedical text mining , 2019, Bioinform..

[14]  Honglei Li,et al.  Biomedical event trigger detection based on convolutional neural network , 2016, Int. J. Data Min. Bioinform..

[15]  Alain C. Vaucher,et al.  Automated extraction of chemical synthesis actions from experimental procedures , 2020, Nature Communications.

[16]  Ayush Kaushal,et al.  IITKGP at W-NUT 2020 Shared Task-1: Domain specific BERT representation for Named Entity Recognition of lab protocol , 2020, W-NUT@EMNLP.

[17]  Kyle Lo,et al.  SciBERT: Pretrained Contextualized Embeddings for Scientific Text , 2019, ArXiv.

[18]  Wolfgang Marwan,et al.  EXACT2: the semantics of biomedical protocols , 2014, BMC Bioinformatics.

[19]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[20]  Laurent Romary,et al.  CamemBERT: a Tasty French Language Model , 2019, ACL.

[21]  Raghu Machiraju,et al.  An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols , 2018, NAACL.

[22]  Omer Levy,et al.  RoBERTa: A Robustly Optimized BERT Pretraining Approach , 2019, ArXiv.

[23]  Patrick Ruch,et al.  BiTeM at WNUT 2020 Shared Task-1: Named Entity Recognition over Wet Lab Protocols using an Ensemble of Contextual Language Models , 2020, WNUT.

[24]  Makoto Miwa,et al.  Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature , 2020, LREC.

[25]  Sampo Pyysalo,et al.  Overview of BioNLP Shared Task 2013 , 2013, BioNLP@ACL.

[26]  Roland Vollgraf,et al.  Contextual String Embeddings for Sequence Labeling , 2018, COLING.

[27]  Hongfei Lin,et al.  Biomedical event trigger detection by dependency-based word embedding , 2015, BIBM.

[28]  Sampo Pyysalo,et al.  Event extraction across multiple levels of biological organization , 2012, Bioinform..

[29]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[30]  Patchigolla V. S. S. Rahul,et al.  Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models , 2017, BioNLP.

[31]  Soroush Vosoughi,et al.  Big Green at WNUT 2020 Shared Task-1: Relation Extraction as Contextualized Sequence Classification , 2020, W-NUT@EMNLP.

[32]  Hiroya Takamura,et al.  mgsohrab at WNUT 2020 Shared Task-1: Neural Exhaustive Approach for Entity and Relation Recognition Over Wet Lab Protocols , 2020, W-NUT@EMNLP.

[33]  Xiaodong Liu,et al.  Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing , 2020, ACM Trans. Comput. Heal..

[34]  Nigel Collier,et al.  Introduction to the Bio-entity Recognition Task at JNLPBA , 2004, NLPBA/BioNLP.

[35]  Deyu Zhou,et al.  Event trigger identification for biomedical events extraction using domain knowledge , 2014, Bioinform..

[36]  Haoding Meng,et al.  Fancy Man Lauches Zippo at WNUT 2020 Shared Task-1: A Bert Case Model for Wet Lab Entity Extraction , 2020, W-NUT@EMNLP.

[37]  Luke S. Zettlemoyer,et al.  Deep Contextualized Word Representations , 2018, NAACL.

[38]  Sampo Pyysalo,et al.  brat: a Web-based Tool for NLP-Assisted Text Annotation , 2012, EACL.

[39]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[40]  William Thies,et al.  Biocoder: A programming language for standardizing and automating biology protocols , 2010, Journal of biological engineering.