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1
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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3
HittER: Hierarchical Transformers for Knowledge Graph Embeddings ...
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4
AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions ...
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5
Corpus-based Open-Domain Event Type Induction ...
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6
Extracting Event Temporal Relations via Hyperbolic Geometry ...
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7
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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16
MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations ...
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17
Lifelong Event Detection with Knowledge Transfer ...
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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 ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.217/ Abstract: Taxonomies are symbolic representations of hierarchical relationships between terms or entities. While taxonomies are useful in broad applications, manually updating or maintaining them is labor-intensive and difficult to scale in practice. Conventional supervised methods for this enrichment task fail to find optimal parents of new terms in low-resource settings where only small taxonomies are available because of overfitting to hierarchical relationships in the taxonomies. To tackle the problem of low-resource taxonomy enrichment, we propose Musubu, an efficient framework for taxonomy enrichment in low-resource settings with pretrained language models (LMs) as knowledge bases to compensate for the shortage of information. Musubu leverages an LM-based classifier to determine whether or not inputted term pairs have hierarchical relationships. Musubu also utilizes Hearst patterns to generate queries to leverage implicit knowledge ...
Keyword: Computational Linguistics; Information Extraction; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
URL: https://dx.doi.org/10.48448/tx33-se70
https://underline.io/lecture/38020-low-resource-taxonomy-enrichment-with-pretrained-language-models
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