Papers with type representation

2 papers
Cross-lingual Structure Transfer for Relation and Event Extraction (D19-1)

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Challenge: Existing approaches to identify complex semantic structures are difficult to train from under-annotated sources.
Approach: They exploit relation- and event-relevant language-universal features to train relation or event extractors from source annotations and apply them to target languages.
Outcome: The proposed approach achieves comparable performance to state-of-the-art models trained on 3,000 manually annotated mentions.
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.

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