Heng Ji, Liliang Ren, Chenkai Sun, Julia Hockenmaier
We lifted 5 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| dwadden/dygiepp | canonical | 0 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| batch_identity | Not yet run | dwadden/dygiepp/dygie/models/shared.py code served (permissive licence) · get_code("bbc5dc5142133340") |
| compute_f1 | Not yet run | dwadden/dygiepp/dygie/training/f1.py code served (permissive licence) · get_code("fada67a6d1459131") |
| cumsum_shifted | Not yet run | dwadden/dygiepp/dygie/models/shared.py code served (permissive licence) · get_code("7e0b52001d4f5323") |
| fields_to_batches | Not yet run | dwadden/dygiepp/dygie/models/shared.py code served (permissive licence) · get_code("1b01fb249fb7cd71") |
| safe_div | Not yet run | dwadden/dygiepp/dygie/training/f1.py code served (permissive licence) · get_code("ac4edaf53cf349f7") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Text-to-Graph extraction aims to automatically extract information graphs consisting of mentions and types from natural language texts. Existing approaches, such as table filling and pairwise scoring, have shown impressive performance on various information extraction tasks, but they are difficult to scale to datasets with longer input texts because of their secondorder space/time complexities with respect to the input length. In this work, we propose a Hybrid SPan GenerAtor (HySPA) that invertibly maps the information graph to an alternating sequence of nodes and edge types, and directly generates such sequences via a hybrid span decoder which can decode both the spans and the types recurrently in linear time and space complexities. Extensive experiments on the ACE05 dataset show that our approach also significantly outperforms state-ofthe-art on the joint entity and relation extraction task. 1
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2106.15838")
get_code_for_paper("2106.15838")
have("2106.15838")
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