Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks (D19-1)
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| Challenge: | Existing methods for inferring the fine-grained type of an entity from knowledge base are incomplete and lack type information. |
| Approach: | They propose a novel Deep Learning architecture to infer the fine-grained type of an entity from a knowledge base. |
| Outcome: | The proposed method significantly outperforms four state-of-the-art methods on two large-scale datasets. |
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Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss (N18-1)
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| Challenge: | Existing methods for fine-grained type classification rely on distant supervision and are susceptible to noisy labels that can be out-of-context or overly-specific. |
| Approach: | They propose a neural network model that uses cross-entropy loss function to handle out-of-context labels and hierarchical loss normalization to cope with overly-specific ones. |
| Outcome: | The proposed model outperforms the state-of-the-art on established benchmarks for the task. |
Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking (P18-1)
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| Challenge: | Existing methods to incorporate hierarchical information into knowledge bases have yielded little benefit. |
| Approach: | They propose methods to integrate hierarchical information using real bilinear mappings . they also propose two new datasets containing wide and deep hierarchies . |
| Outcome: | The proposed methods improve on flat predictions and fine-grained entity typing on FIGER dataset. |
Joint Type Inference on Entities and Relations via Graph Convolutional Networks (P19-1)
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| Challenge: | a novel graph convolutional network (GCN) is proposed for the task of joint entity relation extraction. |
| Approach: | They propose a graph convolutional network running on an entity-relation bipartite graph . they propose combining two different methods to perform joint entity relation extraction . |
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Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing (2022.coling-1)
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| Challenge: | Experimental results show that fine-grained entity typing (FET) can be used to deduce specific semantic types of entities. |
| Approach: | They propose a type-enriched hierarchical contrastive strategy to model type differences . their method can make type information directly perceptible and improve distinguishability . |
| Outcome: | The proposed method can model the differences between hierarchical types and distinguish multi-grained similar types at different granularities. |
Improving Fine-grained Entity Typing with Entity Linking (D19-1)
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| Challenge: | Existing methods for fine-grained entity typing require a large tag set and knowledge of the context. |
| Approach: | They propose a deep neural model that uses context and information from entity linking to improve fine-grained entity typing. |
| Outcome: | The proposed model achieves 5% absolute strict accuracy improvement over the state of the art on two datasets. |
Hierarchical Entity Typing via Multi-level Learning to Rank (2020.acl-main)
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| Challenge: | Named entity recognition (NER) is a canonical information extraction task that assigns spans to one of a handful of types. |
| Approach: | They propose a hierarchical entity classification method that embraces ontological structure at training and during prediction. |
| Outcome: | The proposed method outperforms previous work on strict accuracy and significantly outperformed previous work. |
Fine-grained Entity Typing via Label Reasoning (2021.emnlp-main)
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| Challenge: | Existing approaches to fine-grained entity typing are based on independent classification paradigms, which make them difficult to recognize inter-dependent, long-tailed and fine-granular entities. |
| Approach: | They propose a label reasoning network that exploits label dependencies knowledge entailed in the data. |
| Outcome: | The proposed network can model, learn and reason complex labels in a sequence-to-set, end-to end manner. |
A Chinese Corpus for Fine-grained Entity Typing (2020.lrec-1)
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| Challenge: | Existing datasets for fine-grained entity typing are limited to English . a corpus of 4,800 mentions is manually labeled with free-form entity types . |
| Approach: | They propose a Chinese fine-grained entity typing task that uses crowdsourcing . they categorize each mention into 10 general types and use a large tag set to predict open set of types . |
| Outcome: | The proposed dataset contains 4,800 mentions manually labeled in Chinese . it also categorizes all the fine-grained types into 10 general types . |
Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing (2022.acl-long)
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| Challenge: | Existing models struggle to handle hard mentions due to insufficient contexts, limiting their overall typing performance. |
| Approach: | They propose to exploit sibling mentions to enhance the mention representations by adding unseen test mentions as new nodes for inference. |
| Outcome: | The proposed model outperforms ten strong baseline models and outperformed strong baselines. |
Joint Learning of Representations for Web-tables, Entities and Types using Graph Convolutional Network (2021.eacl-main)
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| Challenge: | Existing approaches for table annotation with entities and types capture the syntactic structure of tables using graphical models or learn embeddings of table entries without accounting for the complete syntaktic structure. |
| Approach: | They propose a Graph Convolutional Network that captures the complete structure of tables, knowledge graph and the training annotations and jointly learns embeddings for table elements as well as the entities and types. |
| Outcome: | The proposed model significantly outperforms state-of-the-art methods on 5 benchmark datasets while showing promising performance on downstream table-related applications. |