ENTYFI: A System for Fine-grained Entity Typing in Fictional Texts (2020.emnlp-demos)
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| Challenge: | ENTYFI is a web-based system for fine-grained typing of entity mentions in fictional texts. |
| Approach: | They propose a web-based system for fine-grained typing of entity mentions in fictional texts . entity types are a core building block of current knowledge bases . |
| Outcome: | The proposed system builds on 205 automatically induced high-quality type systems for popular fictional domains and provides recommendations towards reference type systems. |
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| 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. |
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Sheng Lin, Luye Zheng, Bo Chen, Siliang Tang, Zhigang Chen, Guoping Hu, Yueting Zhuang, Fei Wu, Xiang Ren
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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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| Challenge: | Existing fine-grained entity typing models are criticized for label independence assumption . |
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| Challenge: | Entity typing is the task of assigning semantic types to entities mentioned in text. |
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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. |
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