Submission 486, Chadha/Rashiti/Sibille #60
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Co-authored-by: rashitig <[email protected]> Co-authored-by: Tarun Chadha <[email protected]> Co-authored-by: Christiane Sibille <[email protected]>
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Caution Review failedThe pull request is closed. WalkthroughThe submission introduces a comprehensive study on Named Entity Linking (NEL) in historical research, addressing challenges and methodologies for applying machine learning to historical texts. It outlines a modular workflow utilizing contextual embeddings and various knowledge bases to enhance entity linking accuracy. A new bibliography file has been added, documenting relevant academic references in the field, formatted in BibTeX. Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant NEL_Workflow
participant Knowledge_Base
participant Historical_Texts
User->>NEL_Workflow: Initiate NEL Process
NEL_Workflow->>Historical_Texts: Retrieve historical texts
NEL_Workflow->>Knowledge_Base: Query for entity information
Knowledge_Base-->>NEL_Workflow: Return entity data
NEL_Workflow-->>User: Provide linked entities
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Actionable comments posted: 0
Outside diff range, codebase verification and nitpick comments (1)
submissions/486/index.qmd (1)
42-50: LGTM!The methods section provides a comprehensive overview of the project's approach and key aspects. The modular pipeline and the focus on interpretation of metrics and the role of GLAM institutions are well-explained.
To improve accessibility, consider updating the alt text of the embedded image to provide a more descriptive explanation of the end-to-end Named Entity Linking pipeline. For example:
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Files selected for processing (2)
- submissions/486/index.qmd (1 hunks)
- submissions/486/references.bib (1 hunks)
Files skipped from review due to trivial changes (1)
- submissions/486/references.bib
Additional comments not posted (3)
submissions/486/index.qmd (3)
32-41: LGTM!The introduction section is well-written and provides a clear overview of the research topic. It effectively highlights the challenges and the importance of using contextual embeddings for improved accuracy in NER and NEL tasks.
51-52: LGTM!The conclusion section effectively summarizes the key challenges and the importance of the research. It emphasizes the role of GLAM institutions in providing enriched data layers to users and the need for addressing technical, documentation, and methodological challenges.
1-30: LGTM!The YAML metadata is complete and well-structured. The abstract provides a good overview of the research.
Please ensure that the
references.bibfile is consistent with the citations used in the main content. You can use the following script to verify the consistency:
Pull request
Proposed changes
Co-authored-by: rashitig [email protected]
Co-authored-by: Tarun Chadha [email protected]
Co-authored-by: Christiane Sibille [email protected]
Types of changes
Checklist
Co-authored-by: Name <[email protected]>.Summary by CodeRabbit
New Features
Documentation