Papers with frame-semantic parsing
CogIE: An Information Extraction Toolkit for Bridging Texts and CogNet (2021.acl-demo)
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| Challenge: | CogNet is a knowledge base that integrates three types of knowledge: linguistic knowledge, world knowledge and commonsense knowledge. |
| Approach: | They propose an information extraction toolkit called CogIE that is a bridge connecting raw texts and CogNet. |
| Outcome: | The proposed toolkit can ground raw texts to CogNet and leverage different types of knowledge to enrich extracted results. |
ClaimLens: Automated, Explainable Fact-Checking on Voting Claims Using Frame-Semantics (2024.emnlp-demo)
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| Challenge: | Existing fact-checking solutions lack transparency and explainability . a lack of transparency can make it difficult for users to trust and understand the reasoning behind the outcomes. |
| Approach: | They propose an automated fact-checking system focused on voting-related factual claims that leverages frame-semantic parsing to provide structured and interpretable fact verification. |
| Outcome: | The proposed system can extract relevant information from voting-related factual claims using public records and Vote semantic frame. |
Learning Joint Semantic Parsers from Disjoint Data (N18-1)
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| Challenge: | Various formal meaning representations have been developed corresponding to different semantic theories. |
| Approach: | They propose a method to learn a semantic parser from multiple datasets by treating annotations for unobserved formalisms as latent structured variables. |
| Outcome: | The proposed approach improves on existing methods using unobserved formalisms and underlying corpora. |
Exploiting Definitions for Frame Identification (2021.eacl-main)
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| Challenge: | a frame-semantic parsing task is to determine which frame best captures the meaning of a word or phrase in a sentence. |
| Approach: | They propose a frame identification model that generates representations for frames and lexical units (senses) they evaluate the model on three data sets and show it consistently achieves better performance than previous systems. |
| Outcome: | The proposed model consistently outperforms previous systems on three data sets. |
Robust Frame-Semantic Models with Lexical Unit Trees and Negative Samples (2024.acl-long)
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| Challenge: | Using a RoBERTa-based filter, we achieve an F1 score of 0.775, surpassing the previous state-of-the-art solution by +0.012. |
| Approach: | They propose a new prefix tree modification to enable robust support for multi-word lexical units and a RoBERTa-based filter to achieve an F1 score of 0.775. |
| Outcome: | The proposed model achieves an F1 score of 0.775, surpassing the state-of-the-art model by +0.012. |