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. |
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| Challenge: | Existing methods for fine-grained entity typing require a large tag set and knowledge of the context. |
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EnCore: Fine-Grained Entity Typing by Pre-Training Entity Encoders on Coreference Chains (2024.eacl-long)
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| Challenge: | Entity typing is the task of assigning semantic types to entities mentioned in text. |
| Approach: | They propose to pre-train an entity encoder such that embeddings of coreferring entities are more similar to each other. |
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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 . |
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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. |
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Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition (2020.acl-srw)
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| Challenge: | In general, the labels used in sequence labeling consist of different types of elements. |
| Approach: | They propose to integrate label component information as embeddings into sequence labeling models. |
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Fine-grained Entity Typing without Knowledge Base (2021.emnlp-main)
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| Challenge: | Existing work on fine-grained entity typing (FET) relies on knowledge bases as distant supervision, but lack of or incompleteness of KB can hinder training. |
| Approach: | They propose a two-step framework that trains FET models without accessing any knowledge base. |
| Outcome: | The proposed framework achieves competitive performance with respect to the models trained on the original KB-supervised datasets. |
Efficient Entity Embedding Construction from Type Knowledge for BERT (2022.findings-aacl)
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| Challenge: | Existing work has shown advantages of incorporating knowledge graphs (KGs) into BERT for various NLP tasks. |
| Approach: | They propose to integrate knowledge graphs into BERT to train entity embeddings to include rich information of factual knowledge. |
| Outcome: | The proposed models perform very well when combined with context. |
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 . |
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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KCAT: A Knowledge-Constraint Typing Annotation Tool (P19-3)
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Sheng Lin, Luye Zheng, Bo Chen, Siliang Tang, Zhigang Chen, Guoping Hu, Yueting Zhuang, Fei Wu, Xiang Ren
| Challenge: | Recent years Natural Language Processing community has seen a surge of interest in fine-grained entity typing (FET) given an entity mention (i.e. a sequence of token spans representing an entity), FET aims at uncovering its contextdependent type. |
| Approach: | They propose an efficient Knowledge Constraint Fine-grained Entity Typing Annotation Tool which further improves the entity typing process through entity linking together with some practical functions. |
| Outcome: | The proposed tool improves the entity typing process by linking the candidate types with some practical functions. |