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CNER: Concept and Named Entity Recognition

ConferenceLicense: CC BY-NC 4.0Hugging Face DatasetsHugging Face Model

This is the official repository for CNER: Concept and Named Entity Recognition.

Citation

This work has been published at NAACL 2024 (main conference). If you use any part, please consider citing our paper as follows:

@inproceedings{martinelli-etal-2024-cner,
title = "{CNER}: Concept and Named Entity Recognition",
author = "Martinelli, Giuliano and Molfese, Francesco and Tedeschi, Simone and Fern{\'a}ndez-Castro, Alberte and Navigli, Roberto",
editor = "Duh, Kevin and Gomez, Helena and Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.461",
pages = "8329--8344",
}

Description

This repository contains the evaluation scripts to evaluate CNER models and the official outputs of the CNER system, which can be used to reproduce paper results. We also release:

  • Our silver training and gold evaluation data on Hugging Face.
  • A Concept and Named Entity Recognition model trained on CNER-silver on the Hugging Face🤗 Models hub. Specifically, we fine-tuned a pretrained DeBERTa-v3-base for token classification using the default hyperparameters, optimizer and architecture of Hugging Face (see the Tutorial Notebook), therefore the results of this model may differ from the ones presented in the paper.

Setup

  1. Clone the repository: 
    git clone https://github.com/Babelscape/cner.git
  2. Create a conda environment: 
    conda create -n env-name python==3.9
  3. Install the requirements: 
    pip install -r requirements.txt

Evaluate CNER models

To evaluate a CNER model, run the following script: python scripts/evaluate.py --predictions_path path_to_predictions

where path_to_predictions is a file with CNER predictions over the CNER-gold dataset split.

Supported formats:

  • .jsonl

    {"sentence_id": "55705165.21", "tokens": ["Commander", ..., "."], "predictions": ["B-PER", ... , "O"]}

  • .tsv

    Sentence_idTokenspredictions
    "55705165.21"['Commander', 'Donald', 'S.', ... '.']['B-PER', 'I-PER', ... 'O']

Reproduce Paper Results

At outputs/cner_output.jsonl you can find the official outputs of our CNER system. To reproduce our CNER results, run the following script: python scripts/evaluate.py --predictions_path outputs/cner_output.jsonl

About

CNER: Concept and Named Entity Recognition

Topics

concept-recognitioninformation-extractionmachine-learningnamed-entity-recognitionnlptoken-classification

Resources

Readme
License
Activity
Custom properties

Stars

13 stars

Watchers

3 watching

Forks

1 fork
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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CNER: Concept and Named Entity Recognition
ConferenceLicense: CC BY-NC 4.0Hugging Face DatasetsHugging Face Model
This is the official repository for CNER: Concept and Named Entity Recognition.
Citation
This work has been published at NAACL 2024 (main conference). If you use any part, please consider citing our paper as follows:
@inproceedings{martinelli-etal-2024-cner,
title = "{CNER}: Concept and Named Entity Recognition",
author = "Martinelli, Giuliano and Molfese, Francesco and Tedeschi, Simone and Fern{\'a}ndez-Castro, Alberte and Navigli, Roberto",
editor = "Duh, Kevin and Gomez, Helena and Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.461",
pages = "8329--8344",
}
Description
This repository contains the evaluation scripts to evaluate CNER models and the official outputs of the CNER system, which can be used to reproduce paper results. We also release:
  • Our silver training and gold evaluation data on Hugging Face.
  • A Concept and Named Entity Recognition model trained on CNER-silver on the Hugging Face🤗 Models hub. Specifically, we fine-tuned a pretrained DeBERTa-v3-base for token classification using the default hyperparameters, optimizer and architecture of Hugging Face (see the Tutorial Notebook), therefore the results of this model may differ from the ones presented in the paper.
Setup
  1. Clone the repository:

    git clone https://github.com/Babelscape/cner.git

  2. Create a conda environment:

    conda create -n env-name python==3.9

  3. Install the requirements:

    pip install -r requirements.txt

Evaluate CNER models
To evaluate a CNER model, run the following script:
python scripts/evaluate.py --predictions_path path_to_predictions
where path_to_predictions is a file with CNER predictions over the CNER-gold dataset split.
Supported formats:
  • .jsonl
    {"sentence_id": "55705165.21", "tokens": ["Commander", ..., "."], "predictions": ["B-PER", ... , "O"]}

  • .tsv
    Sentence_idTokenspredictions
    "55705165.21"['Commander', 'Donald', 'S.', ... '.']['B-PER', 'I-PER', ... 'O']
Reproduce Paper Results
At outputs/cner_output.jsonl you can find the official outputs of our CNER system.
To reproduce our CNER results, run the following script:
python scripts/evaluate.py --predictions_path outputs/cner_output.jsonl
About
CNER: Concept and Named Entity Recognition
Topics
concept-recognitioninformation-extractionmachine-learningnamed-entity-recognitionnlptoken-classification
Resources
Readme
License
Activity
Custom properties
Stars
13 stars
Watchers
3 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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CNER: Concept and Named Entity Recognition
ConferenceLicense: CC BY-NC 4.0Hugging Face DatasetsHugging Face Model
This is the official repository for CNER: Concept and Named Entity Recognition.
Citation
This work has been published at NAACL 2024 (main conference). If you use any part, please consider citing our paper as follows:
@inproceedings{martinelli-etal-2024-cner,
title = "{CNER}: Concept and Named Entity Recognition",
author = "Martinelli, Giuliano and Molfese, Francesco and Tedeschi, Simone and Fern{\'a}ndez-Castro, Alberte and Navigli, Roberto",
editor = "Duh, Kevin and Gomez, Helena and Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.461",
pages = "8329--8344",
}
Description
This repository contains the evaluation scripts to evaluate CNER models and the official outputs of the CNER system, which can be used to reproduce paper results. We also release:
  • Our silver training and gold evaluation data on Hugging Face.
  • A Concept and Named Entity Recognition model trained on CNER-silver on the Hugging Face🤗 Models hub. Specifically, we fine-tuned a pretrained DeBERTa-v3-base for token classification using the default hyperparameters, optimizer and architecture of Hugging Face (see the Tutorial Notebook), therefore the results of this model may differ from the ones presented in the paper.
Setup
  1. Clone the repository:

    git clone https://github.com/Babelscape/cner.git

  2. Create a conda environment:

    conda create -n env-name python==3.9

  3. Install the requirements:

    pip install -r requirements.txt

Evaluate CNER models
To evaluate a CNER model, run the following script:
python scripts/evaluate.py --predictions_path path_to_predictions
where path_to_predictions is a file with CNER predictions over the CNER-gold dataset split.
Supported formats:
  • .jsonl
    {"sentence_id": "55705165.21", "tokens": ["Commander", ..., "."], "predictions": ["B-PER", ... , "O"]}

  • .tsv
    Sentence_idTokenspredictions
    "55705165.21"['Commander', 'Donald', 'S.', ... '.']['B-PER', 'I-PER', ... 'O']
Reproduce Paper Results
At outputs/cner_output.jsonl you can find the official outputs of our CNER system.
To reproduce our CNER results, run the following script:
python scripts/evaluate.py --predictions_path outputs/cner_output.jsonl
About
CNER: Concept and Named Entity Recognition
Topics
concept-recognitioninformation-extractionmachine-learningnamed-entity-recognitionnlptoken-classification
Resources
Readme
License
Activity
Custom properties
Stars
13 stars
Watchers
3 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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CNER: Concept and Named Entity Recognition
ConferenceLicense: CC BY-NC 4.0Hugging Face DatasetsHugging Face Model
This is the official repository for CNER: Concept and Named Entity Recognition.
Citation
This work has been published at NAACL 2024 (main conference). If you use any part, please consider citing our paper as follows:
@inproceedings{martinelli-etal-2024-cner,
title = "{CNER}: Concept and Named Entity Recognition",
author = "Martinelli, Giuliano and Molfese, Francesco and Tedeschi, Simone and Fern{\'a}ndez-Castro, Alberte and Navigli, Roberto",
editor = "Duh, Kevin and Gomez, Helena and Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.461",
pages = "8329--8344",
}
Description
This repository contains the evaluation scripts to evaluate CNER models and the official outputs of the CNER system, which can be used to reproduce paper results. We also release:
  • Our silver training and gold evaluation data on Hugging Face.
  • A Concept and Named Entity Recognition model trained on CNER-silver on the Hugging Face🤗 Models hub. Specifically, we fine-tuned a pretrained DeBERTa-v3-base for token classification using the default hyperparameters, optimizer and architecture of Hugging Face (see the Tutorial Notebook), therefore the results of this model may differ from the ones presented in the paper.
Setup
  1. Clone the repository:

    git clone https://github.com/Babelscape/cner.git

  2. Create a conda environment:

    conda create -n env-name python==3.9

  3. Install the requirements:

    pip install -r requirements.txt

Evaluate CNER models
To evaluate a CNER model, run the following script:
python scripts/evaluate.py --predictions_path path_to_predictions
where path_to_predictions is a file with CNER predictions over the CNER-gold dataset split.
Supported formats:
  • .jsonl
    {"sentence_id": "55705165.21", "tokens": ["Commander", ..., "."], "predictions": ["B-PER", ... , "O"]}

  • .tsv
    Sentence_idTokenspredictions
    "55705165.21"['Commander', 'Donald', 'S.', ... '.']['B-PER', 'I-PER', ... 'O']
Reproduce Paper Results
At outputs/cner_output.jsonl you can find the official outputs of our CNER system.
To reproduce our CNER results, run the following script:
python scripts/evaluate.py --predictions_path outputs/cner_output.jsonl
About
CNER: Concept and Named Entity Recognition
Topics
concept-recognitioninformation-extractionmachine-learningnamed-entity-recognitionnlptoken-classification
Resources
Readme
License
Activity
Custom properties
Stars
13 stars
Watchers
3 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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CNER: Concept and Named Entity Recognition
ConferenceLicense: CC BY-NC 4.0Hugging Face DatasetsHugging Face Model
This is the official repository for CNER: Concept and Named Entity Recognition.
Citation
This work has been published at NAACL 2024 (main conference). If you use any part, please consider citing our paper as follows:
@inproceedings{martinelli-etal-2024-cner,
title = "{CNER}: Concept and Named Entity Recognition",
author = "Martinelli, Giuliano and Molfese, Francesco and Tedeschi, Simone and Fern{\'a}ndez-Castro, Alberte and Navigli, Roberto",
editor = "Duh, Kevin and Gomez, Helena and Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.461",
pages = "8329--8344",
}
Description
This repository contains the evaluation scripts to evaluate CNER models and the official outputs of the CNER system, which can be used to reproduce paper results. We also release:
  • Our silver training and gold evaluation data on Hugging Face.
  • A Concept and Named Entity Recognition model trained on CNER-silver on the Hugging Face🤗 Models hub. Specifically, we fine-tuned a pretrained DeBERTa-v3-base for token classification using the default hyperparameters, optimizer and architecture of Hugging Face (see the Tutorial Notebook), therefore the results of this model may differ from the ones presented in the paper.
Setup
  1. Clone the repository:

    git clone https://github.com/Babelscape/cner.git

  2. Create a conda environment:

    conda create -n env-name python==3.9

  3. Install the requirements:

    pip install -r requirements.txt

Evaluate CNER models
To evaluate a CNER model, run the following script:
python scripts/evaluate.py --predictions_path path_to_predictions
where path_to_predictions is a file with CNER predictions over the CNER-gold dataset split.
Supported formats:
  • .jsonl
    {"sentence_id": "55705165.21", "tokens": ["Commander", ..., "."], "predictions": ["B-PER", ... , "O"]}

  • .tsv
    Sentence_idTokenspredictions
    "55705165.21"['Commander', 'Donald', 'S.', ... '.']['B-PER', 'I-PER', ... 'O']
Reproduce Paper Results
At outputs/cner_output.jsonl you can find the official outputs of our CNER system.
To reproduce our CNER results, run the following script:
python scripts/evaluate.py --predictions_path outputs/cner_output.jsonl
About
CNER: Concept and Named Entity Recognition
Topics
concept-recognitioninformation-extractionmachine-learningnamed-entity-recognitionnlptoken-classification
Resources
Readme
License
Activity
Custom properties
Stars
13 stars
Watchers
3 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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CNER: Concept and Named Entity Recognition
ConferenceLicense: CC BY-NC 4.0Hugging Face DatasetsHugging Face Model
This is the official repository for CNER: Concept and Named Entity Recognition.
Citation
This work has been published at NAACL 2024 (main conference). If you use any part, please consider citing our paper as follows:
@inproceedings{martinelli-etal-2024-cner,
title = "{CNER}: Concept and Named Entity Recognition",
author = "Martinelli, Giuliano and Molfese, Francesco and Tedeschi, Simone and Fern{\'a}ndez-Castro, Alberte and Navigli, Roberto",
editor = "Duh, Kevin and Gomez, Helena and Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.461",
pages = "8329--8344",
}
Description
This repository contains the evaluation scripts to evaluate CNER models and the official outputs of the CNER system, which can be used to reproduce paper results. We also release:
  • Our silver training and gold evaluation data on Hugging Face.
  • A Concept and Named Entity Recognition model trained on CNER-silver on the Hugging Face🤗 Models hub. Specifically, we fine-tuned a pretrained DeBERTa-v3-base for token classification using the default hyperparameters, optimizer and architecture of Hugging Face (see the Tutorial Notebook), therefore the results of this model may differ from the ones presented in the paper.
Setup
  1. Clone the repository:

    git clone https://github.com/Babelscape/cner.git

  2. Create a conda environment:

    conda create -n env-name python==3.9

  3. Install the requirements:

    pip install -r requirements.txt

Evaluate CNER models
To evaluate a CNER model, run the following script:
python scripts/evaluate.py --predictions_path path_to_predictions
where path_to_predictions is a file with CNER predictions over the CNER-gold dataset split.
Supported formats:
  • .jsonl
    {"sentence_id": "55705165.21", "tokens": ["Commander", ..., "."], "predictions": ["B-PER", ... , "O"]}

  • .tsv
    Sentence_idTokenspredictions
    "55705165.21"['Commander', 'Donald', 'S.', ... '.']['B-PER', 'I-PER', ... 'O']
Reproduce Paper Results
At outputs/cner_output.jsonl you can find the official outputs of our CNER system.
To reproduce our CNER results, run the following script:
python scripts/evaluate.py --predictions_path outputs/cner_output.jsonl
About
CNER: Concept and Named Entity Recognition
Topics
concept-recognitioninformation-extractionmachine-learningnamed-entity-recognitionnlptoken-classification
Resources
Readme
License
Activity
Custom properties
Stars
13 stars
Watchers
3 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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CNER: Concept and Named Entity Recognition
ConferenceLicense: CC BY-NC 4.0Hugging Face DatasetsHugging Face Model
This is the official repository for CNER: Concept and Named Entity Recognition.
Citation
This work has been published at NAACL 2024 (main conference). If you use any part, please consider citing our paper as follows:
@inproceedings{martinelli-etal-2024-cner,
title = "{CNER}: Concept and Named Entity Recognition",
author = "Martinelli, Giuliano and Molfese, Francesco and Tedeschi, Simone and Fern{\'a}ndez-Castro, Alberte and Navigli, Roberto",
editor = "Duh, Kevin and Gomez, Helena and Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.461",
pages = "8329--8344",
}
Description
This repository contains the evaluation scripts to evaluate CNER models and the official outputs of the CNER system, which can be used to reproduce paper results. We also release:
  • Our silver training and gold evaluation data on Hugging Face.
  • A Concept and Named Entity Recognition model trained on CNER-silver on the Hugging Face🤗 Models hub. Specifically, we fine-tuned a pretrained DeBERTa-v3-base for token classification using the default hyperparameters, optimizer and architecture of Hugging Face (see the Tutorial Notebook), therefore the results of this model may differ from the ones presented in the paper.
Setup
  1. Clone the repository:

    git clone https://github.com/Babelscape/cner.git

  2. Create a conda environment:

    conda create -n env-name python==3.9

  3. Install the requirements:

    pip install -r requirements.txt

Evaluate CNER models
To evaluate a CNER model, run the following script:
python scripts/evaluate.py --predictions_path path_to_predictions
where path_to_predictions is a file with CNER predictions over the CNER-gold dataset split.
Supported formats:
  • .jsonl
    {"sentence_id": "55705165.21", "tokens": ["Commander", ..., "."], "predictions": ["B-PER", ... , "O"]}

  • .tsv
    Sentence_idTokenspredictions
    "55705165.21"['Commander', 'Donald', 'S.', ... '.']['B-PER', 'I-PER', ... 'O']
Reproduce Paper Results
At outputs/cner_output.jsonl you can find the official outputs of our CNER system.
To reproduce our CNER results, run the following script:
python scripts/evaluate.py --predictions_path outputs/cner_output.jsonl
About
CNER: Concept and Named Entity Recognition
Topics
concept-recognitioninformation-extractionmachine-learningnamed-entity-recognitionnlptoken-classification
Resources
Readme
License
Activity
Custom properties
Stars
13 stars
Watchers
3 watching
Forks
1 fork
Report repository
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Used by
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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CNER: Concept and Named Entity Recognition
ConferenceLicense: CC BY-NC 4.0Hugging Face DatasetsHugging Face Model
This is the official repository for CNER: Concept and Named Entity Recognition.
Citation
This work has been published at NAACL 2024 (main conference). If you use any part, please consider citing our paper as follows:
@inproceedings{martinelli-etal-2024-cner,
title = "{CNER}: Concept and Named Entity Recognition",
author = "Martinelli, Giuliano and Molfese, Francesco and Tedeschi, Simone and Fern{\'a}ndez-Castro, Alberte and Navigli, Roberto",
editor = "Duh, Kevin and Gomez, Helena and Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.461",
pages = "8329--8344",
}
Description
This repository contains the evaluation scripts to evaluate CNER models and the official outputs of the CNER system, which can be used to reproduce paper results. We also release:
  • Our silver training and gold evaluation data on Hugging Face.
  • A Concept and Named Entity Recognition model trained on CNER-silver on the Hugging Face🤗 Models hub. Specifically, we fine-tuned a pretrained DeBERTa-v3-base for token classification using the default hyperparameters, optimizer and architecture of Hugging Face (see the Tutorial Notebook), therefore the results of this model may differ from the ones presented in the paper.
Setup
  1. Clone the repository:

    git clone https://github.com/Babelscape/cner.git

  2. Create a conda environment:

    conda create -n env-name python==3.9

  3. Install the requirements:

    pip install -r requirements.txt

Evaluate CNER models
To evaluate a CNER model, run the following script:
python scripts/evaluate.py --predictions_path path_to_predictions
where path_to_predictions is a file with CNER predictions over the CNER-gold dataset split.
Supported formats:
  • .jsonl
    {"sentence_id": "55705165.21", "tokens": ["Commander", ..., "."], "predictions": ["B-PER", ... , "O"]}

  • .tsv
    Sentence_idTokenspredictions
    "55705165.21"['Commander', 'Donald', 'S.', ... '.']['B-PER', 'I-PER', ... 'O']
Reproduce Paper Results
At outputs/cner_output.jsonl you can find the official outputs of our CNER system.
To reproduce our CNER results, run the following script:
python scripts/evaluate.py --predictions_path outputs/cner_output.jsonl
About
CNER: Concept and Named Entity Recognition
Topics
concept-recognitioninformation-extractionmachine-learningnamed-entity-recognitionnlptoken-classification
Resources
Readme
License
Activity
Custom properties
Stars
13 stars
Watchers
3 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages