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Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training ...
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HittER: Hierarchical Transformers for Knowledge Graph Embeddings ...
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AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions ...
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Extracting Event Temporal Relations via Hyperbolic Geometry ...
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Partially Supervised Named Entity Recognition via the Expected Entity Ratio Loss ...
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Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention ...
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An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing ...
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Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification ...
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ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision ...
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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 ...
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Keyword:
Computational Linguistics; Information Extraction; Machine Learning; Machine Learning and Data Mining; Named Entity Recognition; Natural Language Processing
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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
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Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context ...
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Entity Relation Extraction as Dependency Parsing in Visually Rich Documents ...
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MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations ...
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SUBSUME: A Dataset for Subjective Summary Extraction from Wikipedia Documents ...
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Few-Shot Named Entity Recognition: An Empirical Baseline Study ...
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Low-resource Taxonomy Enrichment with Pretrained Language Models ...
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