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Automatic Error Type Annotation for Arabic ...
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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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Corpus-based Open-Domain Event Type Induction ...
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6
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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8
Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention ...
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9
Corrected CBOW Performs as well as Skip-gram ...
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10
An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing ...
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11
Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification ...
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12
ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision ...
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13
Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context ...
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14
TEET! Tunisian Dataset for Toxic Speech Detection ...
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15
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 ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.205/ Abstract: Entity retrieval, which aims at disambiguating mentions to canonical entities from massive KBs, is essential for many tasks in natural language processing. Recent progress in entity retrieval shows that the dual-encoder structure is a powerful and efficient framework to nominate candidates if entities are only identified by descriptions. However, they ignore the property that meanings of entity mentions diverge in different contexts and are related to various portions of descriptions, which are treated equally in previous works. In this work, we propose Multi-View Entity Representations (MuVER), a novel approach for entity retrieval that constructs multi-view representations for entity descriptions and approximates the optimal view for mentions via a heuristic searching method. Our method achieves the state-of-the-art performance on ZESHEL and improves the quality of candidates on three standard Entity Linking datasets ...
Keyword: Computational Linguistics; Information Extraction; Machine Learning; Machine Learning and Data Mining; Named Entity Recognition; Natural Language Processing
URL: https://underline.io/lecture/37445-muver-improving-first-stage-entity-retrieval-with-multi-view-entity-representations
https://dx.doi.org/10.48448/vkcx-tw78
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17
Lifelong Event Detection with Knowledge Transfer ...
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18
SUBSUME: A Dataset for Subjective Summary Extraction from Wikipedia Documents ...
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19
Few-Shot Named Entity Recognition: An Empirical Baseline Study ...
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20
Low-resource Taxonomy Enrichment with Pretrained Language Models ...
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