Update README.md

README.md

@@ -213,18 +213,18 @@ Task 7b will use benchmark datasets containing training and test biomedical ques
 
 |  **Dataset** |         **Task**        | **Train** | **Dev** | **Test** | **Evaluation Metrics** | **Added** |
 |:------------:|:-----------------------:|:---------:|:-------:|:--------:|:----------------------:|-----------|
-|   BC5-chem   |           NER           |    5203   |   5347  |   5385   |     F1 entity-level    |    Yes    |
-|  BC5-disease |           NER           |    4182   |   4244  |   4424   |     F1 entity-level    |    Yes    |
-| NCBI-disease |           NER           |    5134   |   787   |    960   |     F1 entity-level    |    Yes    |
-|     BC2GM    |           NER           |   15197   |   3061  |   6325   |     F1 entity-level    |    Yes    |
-|    JNLPBA    |           NER           |   46750   |   4551  |   8662   |     F1 entity-level    |    Yes    |
+|   BC5-chem   |           NER           |    5203   |   5347  |   5385   |     F1 entity-level    |    **Yes**    |
+|  BC5-disease |           NER           |    4182   |   4244  |   4424   |     F1 entity-level    |    **Yes**    |
+| NCBI-disease |           NER           |    5134   |   787   |    960   |     F1 entity-level    |    **Yes**    |
+|     BC2GM    |           NER           |   15197   |   3061  |   6325   |     F1 entity-level    |    **Yes**    |
+|    JNLPBA    |           NER           |   46750   |   4551  |   8662   |     F1 entity-level    |    **Yes**    |
 |   EBM PICO   |           PICO          |   339167  |  85321  |   16364  |   Macro F1 word-level  |     No    |
 |   ChemProt   |   Relation Extraction   |   18035   |  11268  |   15745  |        Micro F1        |     No    |
 |      DDI     |   Relation Extraction   |   25296   |   2496  |   5716   |        Micro F1        |     No    |
 |      GAD     |   Relation Extraction   |    4261   |   535   |    534   |        Micro F1        |     No    |
-|    BIOSSES   |   Sentence Similarity   |     64    |    16   |    20    |         Pearson        |     Yes   |
+|    BIOSSES   |   Sentence Similarity   |     64    |    16   |    20    |         Pearson        |     **Yes**   |
 |      HoC     | Document Classification |    1295   |   186   |    371   |    Average Micro F1    |     No    |
-|   PubMedQA   |    Question Answering   |    450    |    50   |    500   |        Accuracy        |     Yes   |
+|   PubMedQA   |    Question Answering   |    450    |    50   |    500   |        Accuracy        |     **Yes**   |
 |    BioASQ    |    Question Answering   |    670    |    75   |    140   |        Accuracy        |     No    |
 
 Datasets used in the BLURB biomedical NLP benchmark. The Train, Dev, and test splits might not be exactly identical to those proposed in BLURB.
@@ -274,10 +274,24 @@ English from biomedical texts
   }
   ```
 * **Question Answering**
+
+  * PubMedQA
   ```json
-  {
-    'TBD'
-  }
+{'context': {'contexts': ['Programmed cell death (PCD) is the regulated death of cells within an organism. The lace plant (Aponogeton madagascariensis) produces perforations in its leaves through PCD. The leaves of the plant consist of a latticework of longitudinal and transverse veins enclosing areoles. PCD occurs in the cells at the center of these areoles and progresses outwards, stopping approximately five cells from the vasculature. The role of mitochondria during PCD has been recognized in animals; however, it has been less studied during PCD in plants.',
+   'The following paper elucidates the role of mitochondrial dynamics during developmentally regulated PCD in vivo in A. madagascariensis. A single areole within a window stage leaf (PCD is occurring) was divided into three areas based on the progression of PCD; cells that will not undergo PCD (NPCD), cells in early stages of PCD (EPCD), and cells in late stages of PCD (LPCD). Window stage leaves were stained with the mitochondrial dye MitoTracker Red CMXRos and examined. Mitochondrial dynamics were delineated into four categories (M1-M4) based on characteristics including distribution, motility, and membrane potential (ΔΨm). A TUNEL assay showed fragmented nDNA in a gradient over these mitochondrial stages. Chloroplasts and transvacuolar strands were also examined using live cell imaging. The possible importance of mitochondrial permeability transition pore (PTP) formation during PCD was indirectly examined via in vivo cyclosporine A (CsA) treatment. This treatment resulted in lace plant leaves with a significantly lower number of perforations compared to controls, and that displayed mitochondrial dynamics similar to that of non-PCD cells.'],
+  'labels': ['BACKGROUND', 'RESULTS'],
+  'meshes': ['Alismataceae',
+   'Apoptosis',
+   'Cell Differentiation',
+   'Mitochondria',
+   'Plant Leaves'],
+  'reasoning_free_pred': ['y', 'e', 's'],
+  'reasoning_required_pred': ['y', 'e', 's']},
+ 'final_decision': 'yes',
+ 'long_answer': 'Results depicted mitochondrial dynamics in vivo as PCD progresses within the lace plant, and highlight the correlation of this organelle with other organelles during developmental PCD. To the best of our knowledge, this is the first report of mitochondria and chloroplasts moving on transvacuolar strands to form a ring structure surrounding the nucleus during developmental PCD. Also, for the first time, we have shown the feasibility for the use of CsA in a whole plant system. Overall, our findings implicate the mitochondria as playing a critical and early role in developmentally regulated PCD in the lace plant.',
+ 'pubid': 21645374,
+ 'question': 'Do mitochondria play a role in remodelling lace plant leaves during programmed cell death?'}
+
   ```
 
 
@@ -298,7 +312,12 @@ English from biomedical texts
 * **Document Classification**
   * To be added
 * **Question Answering**
-  * To be added
+  * PubMedQA
+    * `pubid`: integer
+    * `question`: string
+    * `context`: sequence of strings [`contexts`, `labels`, `meshes`, `reasoning_required_pred`, `reasoning_free_pred`]
+    * `long_answer`: string
+    * `final_decision`: string
 
 ### Data Splits
 
@@ -365,35 +384,9 @@ All the datasets have been obtained and annotated by experts in thebiomedical do
 
 ### Citation Information
 
-```json
-{
-  "blurb": """\
-              @article{2022,
-                title={Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing},
-                volume={3},
-                ISSN={2637-8051},
-                url={http://dx.doi.org/10.1145/3458754},
-                DOI={10.1145/3458754},
-                number={1},
-                journal={ACM Transactions on Computing for Healthcare},
-                publisher={Association for Computing Machinery (ACM)},
-                author={Gu, Yu and Tinn, Robert and Cheng, Hao and Lucas, Michael and Usuyama, Naoto and Liu, Xiaodong and Naumann, Tristan and Gao, Jianfeng and Poon, Hoifung},
-                year={2022},
-                month={Jan},
-                pages={1–23} 
-                }
-              """,
-    "BC5CDR-chem-IOB":  """@article{article,
-            author = {Li, Jiao and Sun, Yueping and Johnson, Robin and Sciaky, Daniela and Wei, Chih-Hsuan and Leaman, Robert and Davis, Allan Peter and Mattingly, Carolyn and Wiegers, Thomas and lu, Zhiyong},
-            year = {2016},
-            month = {05},
-            pages = {baw068},
-            title = {BioCreative V CDR task corpus: a resource for chemical disease relation extraction},
-            volume = {2016},
-            journal = {Database},
-            doi = {10.1093/database/baw068}
-            }""",
-    "BC5CDR-disease-IOB":"""@article{article,
+* BC5-chem & BC5-disease
+ ```latex
+ @article{article,
             author = {Li, Jiao and Sun, Yueping and Johnson, Robin and Sciaky, Daniela and Wei, Chih-Hsuan and Leaman, Robert and Davis, Allan Peter and Mattingly, Carolyn and Wiegers, Thomas and lu, Zhiyong},
             year = {2016},
             month = {05},
@@ -402,86 +395,106 @@ All the datasets have been obtained and annotated by experts in thebiomedical do
             volume = {2016},
             journal = {Database},
             doi = {10.1093/database/baw068}
-            }""",
-    "BC2GM-IOB":"""@article{article,
-            author = {Smith, Larry and Tanabe, Lorraine and Ando, Rie and Kuo, Cheng-Ju and Chung, I-Fang and Hsu, Chun-Nan and Lin, Yu-Shi and Klinger, Roman and Friedrich, Christoph and Ganchev, Kuzman and Torii, Manabu and Liu, Hongfang and Haddow, Barry and Struble, Craig and Povinelli, Richard and Vlachos, Andreas and Baumgartner Jr, William and Hunter, Lawrence and Carpenter, Bob and Wilbur, W.},
-            year = {2008},
-            month = {09},
-            pages = {S2},
-            title = {Overview of BioCreative II gene mention recognition},
-            volume = {9 Suppl 2},
-            journal = {Genome biology},
-            doi = {10.1186/gb-2008-9-s2-s2}
-            }""",
-    "NCBI-disease-IOB":"""@article{10.5555/2772763.2772800,
-            author = {Dogan, Rezarta Islamaj and Leaman, Robert and Lu, Zhiyong},
-            title = {NCBI Disease Corpus},
-            year = {2014},
-            issue_date = {February 2014},
-            publisher = {Elsevier Science},
-            address = {San Diego, CA, USA},
-            volume = {47},
-            number = {C},
-            issn = {1532-0464},
-            abstract = {Graphical abstractDisplay Omitted NCBI disease corpus is built as a gold-standard resource for disease recognition.793 PubMed abstracts are annotated with disease mentions and concepts (MeSH/OMIM).14 Annotators produced high consistency level and inter-annotator agreement.Normalization benchmark results demonstrate the utility of the corpus.The corpus is publicly available to the community. Information encoded in natural language in biomedical literature publications is only useful if efficient and reliable ways of accessing and analyzing that information are available. Natural language processing and text mining tools are therefore essential for extracting valuable information, however, the development of powerful, highly effective tools to automatically detect central biomedical concepts such as diseases is conditional on the availability of annotated corpora.This paper presents the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. Each PubMed abstract was manually annotated by two annotators with disease mentions and their corresponding concepts in Medical Subject Headings (MeSH ) or Online Mendelian Inheritance in Man (OMIM ). Manual curation was performed using PubTator, which allowed the use of pre-annotations as a pre-step to manual annotations. Fourteen annotators were randomly paired and differing annotations were discussed for reaching a consensus in two annotation phases. In this setting, a high inter-annotator agreement was observed. Finally, all results were checked against annotations of the rest of the corpus to assure corpus-wide consistency.The public release of the NCBI disease corpus contains 6892 disease mentions, which are mapped to 790 unique disease concepts. Of these, 88% link to a MeSH identifier, while the rest contain an OMIM identifier. We were able to link 91% of the mentions to a single disease concept, while the rest are described as a combination of concepts. In order to help researchers use the corpus to design and test disease identification methods, we have prepared the corpus as training, testing and development sets. To demonstrate its utility, we conducted a benchmarking experiment where we compared three different knowledge-based disease normalization methods with a best performance in F-measure of 63.7%. These results show that the NCBI disease corpus has the potential to significantly improve the state-of-the-art in disease name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks.The NCBI disease corpus, guidelines and other associated resources are available at: http://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/.},
-            journal = {J. of Biomedical Informatics},
-            month = {feb},
-            pages = {1–10},
-            numpages = {10}}""",
-    "JNLPBA":"""@inproceedings{collier-kim-2004-introduction,
-            title = "Introduction to the Bio-entity Recognition Task at {JNLPBA}",
-            author = "Collier, Nigel  and
-              Kim, Jin-Dong",
-            booktitle = "Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications ({NLPBA}/{B}io{NLP})",
-            month = aug # " 28th and 29th",
-            year = "2004",
-            address = "Geneva, Switzerland",
-            publisher = "COLING",
-            url = "https://aclanthology.org/W04-1213",
-            pages = "73--78",
-            }""",
-            
-      "BIOSSES":"""@article{souganciouglu2017biosses,
-                    title={BIOSSES: a semantic sentence similarity estimation system for the biomedical domain},
-                    author={So{\u{g}}anc{\i}o{\u{g}}lu, Gizem and {\"O}zt{\"u}rk, Hakime and {\"O}zg{\"u}r, Arzucan},
-                    journal={Bioinformatics},
-                    volume={33},
-                    number={14},
-                    pages={i49--i58},
-                    year={2017},
-                    publisher={Oxford University Press}
-                  }""",
-                  
-       "PubMedQA":"""@inproceedings{jin2019pubmedqa,
-                        title={PubMedQA: A Dataset for Biomedical Research Question Answering},
-                        author={Jin, Qiao and Dhingra, Bhuwan and Liu, Zhengping and Cohen, William and Lu, Xinghua},
-                        booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
-                        pages={2567--2577},
-                        year={2019}
-                      }
-                             """,
-"BioASQ":"""@article{10.1093/bioinformatics/btv585,
-    author = {Baker, Simon and Silins, Ilona and Guo, Yufan and Ali, Imran and Högberg, Johan and Stenius, Ulla and Korhonen, Anna},
-    title = "{Automatic semantic classification of scientific literature according to the hallmarks of cancer}",
-    journal = {Bioinformatics},
-    volume = {32},
-    number = {3},
-    pages = {432-440},
-    year = {2015},
-    month = {10},
-    abstract = "{Motivation: The hallmarks of cancer have become highly influential in cancer research. They reduce the complexity of cancer into 10 principles (e.g. resisting cell death and sustaining proliferative signaling) that explain the biological capabilities acquired during the development of human tumors. Since new research depends crucially on existing knowledge, technology for semantic classification of scientific literature according to the hallmarks of cancer could greatly support literature review, knowledge discovery and applications in cancer research.Results: We present the first step toward the development of such technology. We introduce a corpus of 1499 PubMed abstracts annotated according to the scientific evidence they provide for the 10 currently known hallmarks of cancer. We use this corpus to train a system that classifies PubMed literature according to the hallmarks. The system uses supervised machine learning and rich features largely based on biomedical text mining. We report good performance in both intrinsic and extrinsic evaluations, demonstrating both the accuracy of the methodology and its potential in supporting practical cancer research. We discuss how this approach could be developed and applied further in the future.Availability and implementation: The corpus of hallmark-annotated PubMed abstracts and the software for classification are available at: http://www.cl.cam.ac.uk/∼sb895/HoC.html .Contact:simon.baker@cl.cam.ac.uk}",
-    issn = {1367-4803},
-    doi = {10.1093/bioinformatics/btv585},
-    url = {https://doi.org/10.1093/bioinformatics/btv585},
-    eprint = {https://academic.oup.com/bioinformatics/article-pdf/32/3/432/19568147/btv585.pdf},
-}
-
-"""
-                    
-  }
+            }
   ```
+* BC2GM 
+ ```latex
+  @article{article,
+              author = {Smith, Larry and Tanabe, Lorraine and Ando, Rie and Kuo, Cheng-Ju and Chung, I-Fang and Hsu, Chun-Nan and Lin, Yu-Shi and Klinger, Roman and Friedrich, Christoph and Ganchev, Kuzman and Torii, Manabu and Liu, Hongfang and Haddow, Barry and Struble, Craig and Povinelli, Richard and Vlachos, Andreas and Baumgartner Jr, William and Hunter, Lawrence and Carpenter, Bob and Wilbur, W.},
+              year = {2008},
+              month = {09},
+              pages = {S2},
+              title = {Overview of BioCreative II gene mention recognition},
+              volume = {9 Suppl 2},
+              journal = {Genome biology},
+              doi = {10.1186/gb-2008-9-s2-s2}
+              }
+ ```
+* JNLPBA
+ ```latex
+    @inproceedings{collier-kim-2004-introduction,
+                title = "Introduction to the Bio-entity Recognition Task at {JNLPBA}",
+                author = "Collier, Nigel  and
+                  Kim, Jin-Dong",
+                booktitle = "Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications ({NLPBA}/{B}io{NLP})",
+                month = aug # " 28th and 29th",
+                year = "2004",
+                address = "Geneva, Switzerland",
+                publisher = "COLING",
+                url = "https://aclanthology.org/W04-1213",
+                pages = "73--78",
+                }
+     ```
+* NCBI Disiease
+ ```latex
+    @article{10.5555/2772763.2772800,
+                author = {Dogan, Rezarta Islamaj and Leaman, Robert and Lu, Zhiyong},
+                title = {NCBI Disease Corpus},
+                year = {2014},
+                issue_date = {February 2014},
+                publisher = {Elsevier Science},
+                address = {San Diego, CA, USA},
+                volume = {47},
+                number = {C},
+                issn = {1532-0464},
+                abstract = {Graphical abstractDisplay Omitted NCBI disease corpus is built as a gold-standard resource for disease recognition.793 PubMed abstracts are annotated with disease mentions and concepts (MeSH/OMIM).14 Annotators produced high consistency level and inter-annotator agreement.Normalization benchmark results demonstrate the utility of the corpus.The corpus is publicly available to the community. Information encoded in natural language in biomedical literature publications is only useful if efficient and reliable ways of accessing and analyzing that information are available. Natural language processing and text mining tools are therefore essential for extracting valuable information, however, the development of powerful, highly effective tools to automatically detect central biomedical concepts such as diseases is conditional on the availability of annotated corpora.This paper presents the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. Each PubMed abstract was manually annotated by two annotators with disease mentions and their corresponding concepts in Medical Subject Headings (MeSH ) or Online Mendelian Inheritance in Man (OMIM ). Manual curation was performed using PubTator, which allowed the use of pre-annotations as a pre-step to manual annotations. Fourteen annotators were randomly paired and differing annotations were discussed for reaching a consensus in two annotation phases. In this setting, a high inter-annotator agreement was observed. Finally, all results were checked against annotations of the rest of the corpus to assure corpus-wide consistency.The public release of the NCBI disease corpus contains 6892 disease mentions, which are mapped to 790 unique disease concepts. Of these, 88% link to a MeSH identifier, while the rest contain an OMIM identifier. We were able to link 91% of the mentions to a single disease concept, while the rest are described as a combination of concepts. In order to help researchers use the corpus to design and test disease identification methods, we have prepared the corpus as training, testing and development sets. To demonstrate its utility, we conducted a benchmarking experiment where we compared three different knowledge-based disease normalization methods with a best performance in F-measure of 63.7%. These results show that the NCBI disease corpus has the potential to significantly improve the state-of-the-art in disease name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks.The NCBI disease corpus, guidelines and other associated resources are available at: http://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/.},
+                journal = {J. of Biomedical Informatics},
+                month = {feb},
+                pages = {1–10},
+                numpages = {10}}
+     ```
+* EBM PICO
+* ChemProt
+* DDI
+* GAD
+* BIOSSES 
+ ```latex
+    @article{souganciouglu2017biosses,
+          title={BIOSSES: a semantic sentence similarity estimation system for the biomedical domain},
+          author={So{\u{g}}anc{\i}o{\u{g}}lu, Gizem and {\"O}zt{\"u}rk, Hakime and {\"O}zg{\"u}r, Arzucan},
+          journal={Bioinformatics},
+          volume={33},
+          number={14},
+          pages={i49--i58},
+          year={2017},
+          publisher={Oxford University Press}
+        }
+
+   ```
+* HoC
+* PubMedQA 
+ ```latex
+  @inproceedings{jin2019pubmedqa,
+                          title={PubMedQA: A Dataset for Biomedical Research Question Answering},
+                          author={Jin, Qiao and Dhingra, Bhuwan and Liu, Zhengping and Cohen, William and Lu, Xinghua},
+                          booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
+                          pages={2567--2577},
+                          year={2019}
+                        }
+   ```
+
+* BioASQ
+ ```latex
+    @article{10.1093/bioinformatics/btv585,
+        author = {Baker, Simon and Silins, Ilona and Guo, Yufan and Ali, Imran and Högberg, Johan and Stenius, Ulla and Korhonen, Anna},
+        title = "{Automatic semantic classification of scientific literature according to the hallmarks of cancer}",
+        journal = {Bioinformatics},
+        volume = {32},
+        number = {3},
+        pages = {432-440},
+        year = {2015},
+        month = {10},
+        abstract = "{Motivation: The hallmarks of cancer have become highly influential in cancer research. They reduce the complexity of cancer into 10 principles (e.g. resisting cell death and sustaining proliferative signaling) that explain the biological capabilities acquired during the development of human tumors. Since new research depends crucially on existing knowledge, technology for semantic classification of scientific literature according to the hallmarks of cancer could greatly support literature review, knowledge discovery and applications in cancer research.Results: We present the first step toward the development of such technology. We introduce a corpus of 1499 PubMed abstracts annotated according to the scientific evidence they provide for the 10 currently known hallmarks of cancer. We use this corpus to train a system that classifies PubMed literature according to the hallmarks. The system uses supervised machine learning and rich features largely based on biomedical text mining. We report good performance in both intrinsic and extrinsic evaluations, demonstrating both the accuracy of the methodology and its potential in supporting practical cancer research. We discuss how this approach could be developed and applied further in the future.Availability and implementation: The corpus of hallmark-annotated PubMed abstracts and the software for classification are available at: http://www.cl.cam.ac.uk/∼sb895/HoC.html .Contact:simon.baker@cl.cam.ac.uk}",
+        issn = {1367-4803},
+        doi = {10.1093/bioinformatics/btv585},
+        url = {https://doi.org/10.1093/bioinformatics/btv585},
+        eprint = {https://academic.oup.com/bioinformatics/article-pdf/32/3/432/19568147/btv585.pdf},
+    }  
+ ```
+
 ### Contributions
 * This dataset has been uploaded and generated by Dr. Jorge Abreu Vicente.
 * Thanks to [@GamalC](https://github.com/GamalC) for uploading the NER datasets to GitHub, from where I got them.
 * I am not part of the team that generated BLURB. This dataset is intended to help researchers to usethe BLURB benchmarking for NLP in Biomedical NLP.
-* Thanks to [@bwang482](https://github.com/bwang482) for uploading the [BIOSSES dataset](https://github.com/bwang482/datasets/tree/master/datasets/biosses). We forked the [BIOSSES 🤗 dataset](https://huggingface.co/datasets/biosses) to add it to this BLURB benchmark.
+* Thanks to [@bwang482](https://github.com/bwang482) for uploading the [BIOSSES dataset](https://github.com/bwang482/datasets/tree/master/datasets/biosses). We forked the [BIOSSES 🤗 dataset](https://huggingface.co/datasets/biosses) to add it to this BLURB benchmark.
+* Thank you to [@tuner007](https://github.com/tuner007) for adding this dataset to the 🤗 hub