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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 7 of 7 1 ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Garg, Shweta; Han, Jiawei; Hu, Vivian; Song, Xiangchen; Wang, Xuan; Xiao, Jinfeng. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.424/ Abstract: Scientific literature analysis needs fine-grained named entity recognition (NER) to provide a wide range of information for scientific discovery. For example, chemistry research needs to study dozens to hundreds of distinct, fine-grained entity types, making consistent and accurate annotation difficult even for crowds of domain experts. On the other hand, domain-specific ontologies and knowledge bases (KBs) can be easily accessed, constructed, or integrated, which makes distant supervision realistic for fine-grained chemistry NER. In distant supervision, training labels are generated by matching mentions in a document with the concepts in the knowledge bases (KBs). However, this kind of KB-matching suffers from two major challenges: incomplete annotation and noisy annotation. We propose ChemNER, an ontology-guided, distantly supervised method for fine-grained chemistry NER to tackle these challenges. It leverages the chemistry type ... Keyword: Computational Linguistics; Information Extraction; Machine Learning; Machine Learning and Data Mining; Named Entity Recognition; Natural Language Processing URL: https://underline.io/lecture/37360-chemner-fine-grained-chemistry-named-entity-recognition-with-ontology-guided-distant-supervision https://dx.doi.org/10.48448/nr6v-8309 BASE Hide details 2 MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ma, Xinyin. - : Underline Science Inc., 2021 BASE Show details 3 Few-Shot Named Entity Recognition: An Empirical Baseline Study ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Balakrishnan, Shobana; Chen, Weizhu. - : Underline Science Inc., 2021 BASE Show details 4 Data Augmentation for Cross-Domain Named Entity Recognition ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Gustavo; Chen, Shuguang. - : Underline Science Inc., 2021 BASE Show details 5 A Bag of Tricks for Dialogue Summarization ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ballesteros, Miguel; Khalifa, Muhammad. - : Underline Science Inc., 2021 BASE Show details 6 A Partition Filter Network for Joint Entity and Relation Extraction ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Fu, Jinlan; Wei, Zhongyu. - : Underline Science Inc., 2021 BASE Show details 7 Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Gallinari, Patrick; Guigue, Vincent. - : Underline Science Inc., 2021 BASE Show details

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