| 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. |
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| Challenge: | Existing methods for fine-grained entity typing use weak labels that are automatically generated. |
| Approach: | They propose to obtain training data by using a BERT Masked Language Model (MLM) given a mention in a sentence, they construct an input for the MLM so it predicts context dependent hypernyms of the mention, which can be used as type labels. |
| Outcome: | The proposed model improves performance by using type labels generated from a BERT Masked Language Model given a mention in a sentence. |
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. |
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Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference (2022.tacl-1)
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| Challenge: | Existing methods for ultra-fine entity typing fail to capture type semantics because of the large number of types and the scarcity of data per type. |
| Approach: | They propose a method that formulates entity typing as a natural language inference problem . they use indirect supervision from NLI to infer type information as textual hypotheses . |
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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 . |
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From Ultra-Fine to Fine: Fine-tuning Ultra-Fine Entity Typing Models to Fine-grained (2023.acl-long)
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| Challenge: | Existing approaches to fine-grained entity typing are limited by the errors in the annotation process. |
| Approach: | They propose a method that can be used to fine-tune a model to a new type schema without creating distantly labeled data. |
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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 model’s decision that the entity belongs to the corresponding type. |
Ultra-Fine Entity Typing with Prior Knowledge about Labels: A Simple Clustering Based Strategy (2023.findings-emnlp)
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| Challenge: | Ultra-fine entity typing is a task of inferring the semantic types from a large set of fine-grained candidates that apply to a given entity mention. |
| Approach: | They propose to use pre-trained label embeddings to cluster the labels into semantic domains and treat them as additional types. |
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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. |
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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. |
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Fine-grained Typing of Emerging Entities in Microblogs (2021.findings-emnlp)
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| Challenge: | Graus et al., 2018) defined emerging entities as those that appear in contexts that emphasize their novelty, and attempted to discover emerging entities from microblogs. |
| Approach: | They propose a task that assigns a fine-grained type to each emerging entity when a burst of posts containing that entity is first observed in a microblog. |
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