An Attentive Fine-Grained Entity Typing Model with Latent Type Representation (D19-1)
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| Challenge: | Existing fine-grained entity typing models are criticized for label independence assumption . |
| Approach: | They propose a fine-grained entity typing model with a new attention mechanism and a hybrid type classifier to exploit type inter-dependency with latent type representation. |
| Outcome: | The proposed model significantly advances the state-of-the-art on fine-grained entity typing. |
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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. |
Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge Bases (C18-1)
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| Challenge: | Existing methods for identifying semantic type of entities are incomplete even in large knowledge bases. |
| Approach: | They propose an attributed and predictive entity embedding method which can fully utilize various kinds of information comprehensively. |
| Outcome: | Experiments on two real DBpedia datasets show that the proposed method outperforms 8 state-of-the-art methods with 4.0% improvement in Mi-F1 and 5.2% improvement in Ma-F1. |
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. |
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 . |
Interpretable Entity Representations through Large-Scale Typing (2020.findings-emnlp)
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| Challenge: | In traditional methods for natural language processing, entities are embedded in dense vector spaces with pre-trained models. |
| Approach: | They propose an approach to creating entity representations that are human readable and achieve high performance on entity-related tasks out of the box. |
| Outcome: | The proposed representations are vectors whose values correspond to posterior probabilities over fine-grained entity types, indicating the confidence of a typing modelās decision that the entity belongs to the corresponding type. |
Modeling Fine-Grained Entity Types with Box Embeddings (2021.acl-long)
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| Challenge: | Neural entity typing models typically represent fine-grained entity types as vectors in a high-dimensional space, but such spaces are not well-suited to modeling complex interdependencies. |
| Approach: | They propose to use box embeddings to embed types into a high-dimensional hyperrectangle space and then use it to hypothesize a type representation for the mention. |
| Outcome: | The proposed model captures latent type hierarchies better than a vector-based model on several entity typing benchmarks. |
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. |
Ultra-Fine Entity Typing (P18-1)
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| Challenge: | Experimental results show that a model that can predict ultra-fine types can be crowd-sourced . head words indicate the type of the noun phrases they appear in, and are important for context-sensitive tasks . |
| Approach: | They propose a task where sentences are given with an entity mention . they introduce a new type of distant supervision: head words, which indicate the type of noun phrases they appear in. |
| Outcome: | The proposed model can predict ultra-fine types at varying granularity and performs well on a fine-grained entity typing benchmark. |
Description-Based Zero-shot Fine-Grained Entity Typing (N19-1)
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| 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. |
Coarse-to-Fine Pre-training for Named Entity Recognition (2020.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) is a task of discovering information entities and identifying their corresponding categories. |
| Approach: | They propose a NER-specific framework to inject coarse-to-fine named entity knowledge into pre-trained models by using a remote supervision strategy. |
| Outcome: | The proposed framework achieves significant improvements against several pre-trained base-lines, demonstrating its effectiveness in label-few and low-resource scenarios. |