| Challenge: | BioNLP-OST is an international competition organized to facilitate development and sharing of computational tasks of biomedical text mining and solutions to them. |
| Approach: | They propose a new mental health informatics task that is composed of two subtasks: information retrieval and sentence extraction. |
| Outcome: | The proposed task performed well on both tasks, but there are still challenges. |
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BioNLP-OST 2019 RDoC Tasks: Multi-grain Neural Relevance Ranking Using Topics and Attention Based Query-Document-Sentence Interactions (D19-57)
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| Challenge: | Our best systems achieved 1st rank and scored 0.86 mAP and 0.58 macro average accuracy in Task-1 and Task-2 respectively. |
| Approach: | They propose to use attention-based supervised neural topic model and SVM for retrieval and ranking of PubMed abstracts and to use BM25 and other relevance measures for re-ranking. |
| Outcome: | The proposed system scored 0.86 mAP and 0.58 macro average accuracy in the RDoC Tasks of BioNLP-OST 2019 . |
Bacteria Biotope at BioNLP Open Shared Tasks 2019 (D19-57)
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| Challenge: | The Bacteria Biotope task focuses on the extraction of the locations and phenotypes of microorganisms from PubMed abstracts and full-text excerpts. |
| Approach: | They propose to use PubMed abstracts and full-text excerpts to extract the locations and phenotypes of microorganisms and to characterizations of these entities with respect to reference knowledge sources. |
| Outcome: | The proposed subtasks, the corpus characteristics, and the challenge organization are compared with the previous edition in 2016 and the results are presented in the second edition. |
Proceedings of the 5th Workshop on BioNLP Open Shared Tasks (D19-57)
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| Challenge: | a workshop organized by BioNLP-ST aims to share computational tasks of biomedical text mining and solutions to them. |
| Approach: | this year, six tasks are contributed by voluntary task organizers . they aim to promote the sharing of computational tasks of biomedical text mining . 43 reviewers selected 30 papers to be presented for the workshop . |
| Outcome: | the BioNLP Open Shared Tasks is organized to promote the sharing of computational tasks of biomedical text mining and solutions to them. |
CRAFT Shared Tasks 2019 Overview — Integrated Structure, Semantics, and Coreference (D19-57)
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William Baumgartner, Michael Bada, Sampo Pyysalo, Manuel R. Ciosici, Negacy Hailu, Harrison Pielke-Lombardo, Michael Regan, Lawrence Hunter
| Challenge: | CRAFT corpus provides a unique foundation for integrating natural language processing (NLP) tasks involving structure, semantics, and coreference. |
| Approach: | They propose to use the CRAFT corpus to evaluate three fundamental language processing tasks over full-text biomedical articles. |
| Outcome: | The CRAFT corpus provides a unique foundation for integrating natural language processing tasks involving structure, semantics, and coreference. |
An Overview of the Active Gene Annotation Corpus and the BioNLP OST 2019 AGAC Track Tasks (D19-57)
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| Challenge: | Biomedical natural language processing (BioNLP) has long been recognized as effective method to accelerate drug-related knowledge discovery. |
| Approach: | They developed an active gene annotation corpus (AGAC) to support drug repurposing. |
| Outcome: | The active gene annotation corpus (AGAC) was developed to support knowledge discovery for drug repurposing. |
Integration of Deep Learning and Traditional Machine Learning for Knowledge Extraction from Biomedical Literature (D19-57)
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| Challenge: | BB system is among the top two systems in five of all six subtasks . knowledge about microbial diversity is crucial for the study of microbiome and bacteria . |
| Approach: | They present a system that uses word embedding and lexical features to perform entities recognition, normalization and relation extraction. |
| Outcome: | The proposed system achieves state-of-the-art in five of six subtasks and is among the top two in five. |
Biomedical relation extraction with pre-trained language representations and minimal task-specific architecture (D19-57)
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| Challenge: | Using a pre-trained BERT-Base model, we learn domain-specific language representations using biomedical text. |
| Approach: | They propose a system that extends BERT, a state-of-the-art language model, which learns contextual language representations from a large unlabelled corpus. |
| Outcome: | The proposed model outperforms a baseline model while relying on an extremely simple setup with no specially engineered features. |
BOUN-ISIK Participation: An Unsupervised Approach for the Named Entity Normalization and Relation Extraction of Bacteria Biotopes (D19-57)
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| Challenge: | In 2011, the Bacteria Biotope Task was conducted for the first time as a part of the BioNLP Shared Task targeting the extraction of useful information regarding bacteria and their habitats. |
| Approach: | They propose two systems for the normalization of entities and the identification of relations between entities given a biomedical text. |
| Outcome: | The proposed method performs as good as deep learning based methods which require labeled data. |
Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection (D19-50)
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| Challenge: | A shared task on fine-grained propaganda detection was organized at EMNLP-IJCNLP 2019 . 12 systems submitted systems for the FLC task, 25 for the SLC task, and 14 teams submitted a system description paper . |
| Approach: | They present a task on fine-grained propaganda detection as part of the NLP4IF workshop at EMNLP-IJCNLP 2019 . they used a corpus of news articles annotated with an inventory of propagandist techniques at the fragment level to determine the propaganda technique used in each fragment . |
| Outcome: | The shared task on fine-grained propaganda detection was organized at the EMNLP-IJCNLP 2019 . 12 systems submitted for the FLC task, 25 for the SLC task, and 14 submitted a system description paper . |
MentalHelp: A Multi-Task Dataset for Mental Health in Social Media (2024.lrec-main)
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Nishat Raihan, Sadiya Sayara Chowdhury Puspo, Shafkat Farabi, Ana-Maria Bucur, Tharindu Ranasinghe, Marcos Zampieri
| Challenge: | Annotating social media data for mental health disorders is expensive and time-consuming, limiting their size and scope. |
| Approach: | They present a large-scale semi-supervised mental disorder detection dataset containing 14 million instances from Reddit and an ensemble of three separate models. |
| Outcome: | The proposed dataset contains 14 million instances of mental disorders . it was collected from reddit and labeled in a semi-supervised way . |