Papers by Ida Szubert
The Role of Reentrancies in Abstract Meaning Representation Parsing (2020.findings-emnlp)
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| Challenge: | Abstract Meaning Representation (AMR) parsers make errors with respect to reentrancies, which complicates AMR parsing and requires specific transitions. |
| Approach: | They propose to categorize the types of errors AMR parsers make with respect to reentrancies and find that correcting these errors provides an in-crease of up to 5% Smatch in parsing perfor- mance and 20% in reen- trancy prediction. |
| Outcome: | The proposed formalism can predict reentrancies with 5% accuracy and 20% accuracy. |
Node Embeddings for Graph Merging: Case of Knowledge Graph Construction (D19-53)
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| Challenge: | Combining two graphs requires merging the nodes which are counterparts of each other. In this process errors occur, resulting in incorrect merging or failure to merge. |
| Approach: | They propose to replace string similarity with vector embedding similarity to reduce errors when merging two graphs . they propose to use graph-based and word-based embeddable graph embeddances to obtain graph node embeddations. |
| Outcome: | The proposed algorithm reduces errors in merging two graphs with a corpus level one graph using string similarity and vector embedding similarity. |
A Structured Syntax-Semantics Interface for English-AMR Alignment (N18-1)
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| Challenge: | Abstract Meaning Representation (AMR) annotations do not require explicit mapping between elements of an AMR and the corresponding elements of the sentence that evoke them. |
| Approach: | They devised an expressive framework to align AMR graphs to dependency graphs . their framework explains how 97% of AMR edges are evoked by words or syntax . |
| Outcome: | The proposed framework explains how 97% of AMR edges are evoked by words or syntax. |