Papers with FIGER
Description-Based Zero-shot Fine-Grained Entity Typing (N19-1)
Copied to clipboard
| Challenge: | Existing systems consider a small set of coarse types, but fine-grained Entity Typing can be used for a variety of tasks. |
| Approach: | They propose a zero-shot entity typing approach that utilizes the type description available from Wikipedia to build a distributed semantic representation of the types. |
| Outcome: | The proposed method is able to recognize novel types without additional training on a public benchmark dataset. |
Cross-lingual Inference with A Chinese Entailment Graph (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing work on predicate entailment detection from typed open relation triples has not been able to detect predicates. |
| Approach: | They propose a pipeline for building Chinese entailment graphs using an open relation extraction method. |
| Outcome: | The proposed pipeline outperforms monolingual and Chinese entailment graphs on a parallel dataset. |
Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing (2022.coling-1)
Copied to clipboard
| 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. |
Transforming Wikipedia into a Large-Scale Fine-Grained Entity Type Corpus (L18-1)
Copied to clipboard
| Challenge: | et al. (2017): WiFiNE annotated with fine-grained entity types . lack of a well-established training corpus makes it difficult to manually annotate the amount of data needed for training. |
| Approach: | They propose an English corpus annotated with fine-grained entity types based on Wikipedia . they use heuristics to build a large, high quality, annotating corpus using 2 manually annotized benchmarks . |
| Outcome: | The proposed system outperforms the existing systems with two datasets and gains a 2.8 macro F1 score. |
Incorporating Object-Level Visual Context for Multimodal Fine-Grained Entity Typing (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Experimental results show that fine-grained entity typing is superior to text-based methods. |
| Approach: | They propose a task called fine-grained entity typing to classify entities . they propose combining textual and visual contexts to capture fine-granular semantic information . |
| Outcome: | The proposed approach achieves superior classification performance compared to previous text-based approaches. |