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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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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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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.218/ Abstract: Previous works on key information extraction from visually rich documents (VRDs) mainly focus on labeling the text within each bounding box (i.e., semantic entity), while the relations in-between are largely unexplored. In this paper, we adapt the popular dependency parsing model, the biaffine parser, to this entity relation extraction task. Being different from the original dependency parsing model which recognizes dependency relations between words, we identify relations between groups of words with layout information instead. We have compared different representations of the semantic entity, different VRD encoders, and different relation decoders. For the model training, we explore multi-task learning to combine entity labeling and relation extraction tasks; and for the evaluation, we conduct experiments on different datasets with filtering and augmentation. The results demonstrate that our proposed model achieves 65.96% F1 ...
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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/wb4w-rr65 https://underline.io/lecture/38076-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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