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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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Abstract:
We study learning named entity recognizers in the presence of missing entity annotations. We approach this setting as tagging with latent variables and propose a novel loss, the Expected Entity Ratio, to learn models in the presence of systematically missing tags. We show that our approach is both theoretically sound and empirically useful. Experimentally, we find that it meets or exceeds performance of strong and state-of-the-art baselines across a variety of languages, annotation scenarios, and amounts of labeled data. In particular, we find that it significantly outperforms the previous state-of-the-art methods from [Mayhew et al. '19] and [Li et al. '21] by +12.7 and +2.3 F1 score in a challenging setting with only 1,000 biased annotations, averaged across 7 datasets. We also show that, when combined with our approach, a novel sparse annotation scheme outperforms exhaustive annotation for modest annotation budgets. We have published our implementation and experimental results at ...
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Keyword:
Computational Linguistics; Information Extraction; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
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URL: https://dx.doi.org/10.48448/xhsd-jp35 https://underline.io/lecture/38200-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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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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