Papers with biomedical text mining
Negation typology and general representation models for cross-lingual zero-shot negation scope resolution in Russian, French, and Spanish. (2021.naacl-srw)
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| Challenge: | Negation resolution remains an acute and continuously researched question in Natural Language Processing. |
| Approach: | They propose to use multilingual pre-trained general representation models to detect negation scope in languages without annotated data. |
| Outcome: | The proposed model achieves token-level F1 score between English, Spanish, French, and Russian. |
Knowledge-enhanced Response Generation in Dialogue Systems: Current Advancements and Emerging Horizons (2024.lrec-tutorials)
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| Challenge: | Knowledge-enhanced Dialogue Systems (KEDS) are a new approach to enhancing human-machine interaction through natural language. |
| Approach: | This tutorial provides an in-depth exploration of Knowledge-enhanced Dialogue Systems (KEDS) it aims to elucidate their significance, highlight advances made using deep learning, and pinpoint the current challenges. |
| Outcome: | The tutorial aims to give attendees a comprehensive understanding of KEDS, and highlight advances made using deep learning and pinpoint the current challenges. |
RDoC Task at BioNLP-OST 2019 (D19-57)
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| 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. |
Document-level Biomedical Relation Extraction Based on Multi-Dimensional Fusion Information and Multi-Granularity Logical Reasoning (2022.coling-1)
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| Challenge: | Existing models with reasoning are single-granularity based on one element information, ignoring complementary fact of different granularities. |
| Approach: | They propose a document-level biomedical relation extraction model called FILR . it uses multi-dimensional information fusion and multi-granularity logic to obtain rich inferences . |
| Outcome: | The proposed model extracts all relation facts from biomedical documents . it is based on multi-dimensional information fusion and multi-granularity logic reasoning . the proposed model achieves state-of-the-art performance on two widely used biomedically corpora . |
BioRo: The Biomedical Corpus for the Romanian Language (L18-1)
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| Challenge: | Biomedical text mining uses linguistic resources available in English, but for other languages such as Romanian, the access to language resources is not straight-forward. |
| Approach: | They present a biomedical corpus of the Romanian language, which is a valuable linguistic asset for biomedically text mining. |
| Outcome: | The proposed corpus will be made publicly available to the biomedical text mining community . the corpus is a reference corpus for the Romanian language . |
Infrastructure for Semantic Annotation in the Genomics Domain (2020.lrec-1)
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Mahmoud El-Haj, Nathan Rutherford, Matthew Coole, Ignatius Ezeani, Sheryl Prentice, Nancy Ide, Jo Knight, Scott Piao, John Mariani, Paul Rayson, Keith Suderman
| Challenge: | a novel infrastructure for biomedical text mining combines NLP and corpus linguistics methods to provide a comprehensive corpus for literature-based discovery. |
| Approach: | They propose a novel pipeline for the collection, annotation, storage, retrieval and analysis of biomedical and life sciences literature . it uses an updatable Gene Ontology Semantic Tagger and a NLP pipeline scheduler to collect and process the corpus. |
| Outcome: | The proposed infrastructure allows for extreme-scale research on the open access PubMed Central archive. |