Update README.md

README.md

@@ -82,25 +82,27 @@ Each instance of the dataset has the following fields and the following types of
 
 ```json
 {
-  "id": "d001",
-  "sentence_id": "d001.s001",
-  "surface_forms": ['Le', 'groupe', 'des', 'Nations_Unies', 'a', 'des', 'projets', 'de', 'plans', 'pour', 'la', 'réduction', 'des', 'émissions'],
-  "labels": ['<NONE>', 'group%1:03:00::;grouping%1:03:00::', '<NONE>', 'un%1:14:00::;united_nations%1:14:00::', '<NONE>', '<NONE>', '<NONE>', '<NONE>', 'programme%1:09:00::;plan%1:09:00::;program%1:09:00::', '<NONE>', '<NONE>', 'step-down%1:04:00::;decrease%1:04:00::;reduction%1:04:00::;diminution%1:04:00::', '<NONE>', 'emanation%1:04:00::;emission%1:04:00::'],
-  "first_labels": ['<NONE>', 'group%1:03:00', '<NONE>', 'un%1:14:00', '<NONE>', '<NONE>', '<NONE>', '<NONE>', 'programme%1:09:00', '<NONE>', '<NONE>', 'step-down%1:04:00', '<NONE>', 'emanation%1:04:00'],
-  "word_id": ['<NONE>', 'd001.s001.t001', '<NONE>', 'd001.s001.t002', '<NONE>', '<NONE>', 'd001.s001.t003', '<NONE>', 'd001.s001.t004', '<NONE>', '<NONE>', 'd001.s001.t005', '<NONE>', 'd001.s001.t006'],
-  "lemmas": ['le', 'groupe', 'du', 'nations_unies', 'avoir', 'du', 'projet', 'de', 'plan', 'pour', 'le', 'réduction', 'du', 'émission'],
-  "pos": ['DET:ART', 'NOM', 'PRP:det', 'NE', 'VER:pres', 'PRP:det', 'NOM', 'PRP', 'NOM', 'PRP', 'DET:ART', 'NOM', 'PRP:det', 'NOM']
+        "id": "621",
+        "topic_id": "2",
+        "statement_medical": "A 32-year-old woman comes to the hospital with vaginal spotting.  Her last menstrual period was 10 weeks ago. She has regular menses lasting for 6 days and repeating every 29 days. Medical history is significant for appendectomy and several complicated UTIs. She has multiple male partners, and she is inconsistent with using barrier contraceptives. Vital signs are normal.  Serum \u03b2-hCG level is 1800 mIU/mL, and a repeat level after 2 days shows an abnormal rise to 2100 mIU/mL.  Pelvic ultrasound reveals a thin endometrium with no gestational sac in the uterus.",
+        "statement_pol": "I just turned 32 and last morning I woke up with strange blood stains on my underwear. My last periods were more than 2 months ago, which is unusual for me because I used to have regular periods lasting for 6 days every 29 days, more or less. I had several UTIs in the past. I also had appendicitis. I'm currently seeing several men and, to be honest, some of them do struggle to wear a condom. I went to the hospital to check myself up and they told me that my vitals were normal. I also had a blood test on Monday, and my \u03b2-hCG level was 1800 mIU/mL, and then on Wednesday, it went up to 2100 mIU/mL. The gynecologist also did an ultrasound and she told me that, hopefully, there was no ovule.",
+        "premise": "Inclusion Criteria:\n\n          -  women with PUL\n\n        Exclusion Criteria:\nFemale\nAccepts Healthy Volunteers\n\n",
+        "NCT_title": "Hysteroscopy for Pregnancy of Unknown Location",
+        "NCT_id": "NCT02637739",
+        "label": "Entailment"
 }
 
 ```
 
 ### Data Fields
 
-Each sentence has the following fields: **document_id**, **sentence_id**, **surface_forms**, **labels**, **first_labels**, **word_id**, **lemmas**, **pos**. 
+Each instance has the following fields: **id**, **topic_id**, **statement_medical**, **statement_pol**, **premise**, **NCT_title**, **NCT_id**, **label**. 
 
 ### Data Splits
 
-No splits provided. 
+Train: 4904 instances
+Validation: 525 instances
+Test: 1578 instances
 
 ## Dataset Creation
 
@@ -108,53 +110,49 @@ No splits provided.
 
 #### Initial Data Collection and Normalization
 
-[More Information Needed]
+_premise_ (CTRs) and *statement_medical* taken from TREC-CT 2022. 
 
 
 ### Annotations
 
 #### Annotation process
 
-To annotate FrenchSemEval, the annotators used [WebAnno](https://webanno.github.io/webanno/) an open-source adaptable annotation tool. Sentences have been pre-processed into CoNLL format and then annotated into WebAnno. The annotators where asked to only annotate marked occurences using the sense inventory from Wiktionnary. 
+Automatic mapping of TREC-CT 2022's ranking to NLI annotations. _eligible_ mapped as _Entailment_ and _excluded_ mapped as _Contradiction_. 
 
-#### Who are the annotators?
+Manual rephrasing of original *statement_medical*
+
+### Annotators
+
+Paper's first author.
 
-The annotation has been performed by 3 French students, with no prior experience in dataset annotation. 
 
 ### Dataset statistics 
 
-|Type|#| 
-|---|---|
-|Number of sentences|3121| 
-|  Number of annoatated verb tokens | 3199  |  
-|  Number of annotated verb types | 66  | 
-|  Mean number of annotations per verb type  | 48.47  | 
-|  Mean number of senses per verb type | 3.83 |
+|Split|# Entailment| # Contradiction |
+|---|---|---|
+|Train|2757| 2147 |
+|  Dev. | 295  | 230 | 
+| Test | 887  | 691 |
+
 
 ### Licensing Information
 
 ```
-GNU Lesser General Public License
+MIT
 ```
 
 ### Citation Information
 
 ```bibtex
-@inproceedings{segonne-etal-2019-using,
-    title = "Using {W}iktionary as a resource for {WSD} : the case of {F}rench verbs",
-    author = "Segonne, Vincent  and
-      Candito, Marie  and
-      Crabb{\'e}, Beno{\^\i}t",
-    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
-    month = may,
-    year = "2019",
-    address = "Gothenburg, Sweden",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/W19-0422",
-    doi = "10.18653/v1/W19-0422",
-    pages = "259--270",
-    abstract = "As opposed to word sense induction, word sense disambiguation (WSD) has the advantage of us-ing interpretable senses, but requires annotated data, which are quite rare for most languages except English (Miller et al. 1993; Fellbaum, 1998). In this paper, we investigate which strategy to adopt to achieve WSD for languages lacking data that was annotated specifically for the task, focusing on the particular case of verb disambiguation in French. We first study the usability of Eurosense (Bovi et al. 2017) , a multilingual corpus extracted from Europarl (Kohen, 2005) and automatically annotated with BabelNet (Navigli and Ponzetto, 2010) senses. Such a resource opened up the way to supervised and semi-supervised WSD for resourceless languages like French. While this perspective looked promising, our evaluation on French verbs was inconclusive and showed the annotated senses{'} quality was not sufficient for supervised WSD on French verbs. Instead, we propose to use Wiktionary, a collaboratively edited, multilingual online dictionary, as a resource for WSD. Wiktionary provides both sense inventory and manually sense tagged examples which can be used to train supervised and semi-supervised WSD systems. Yet, because senses{'} distribution differ in lexicographic examples found in Wiktionary with respect to natural text, we then focus on studying the impact on WSD of the training data size and senses{'} distribution. Using state-of-the art semi-supervised systems, we report experiments of Wiktionary-based WSD for French verbs, evaluated on FrenchSemEval (FSE), a new dataset of French verbs manually annotated with wiktionary senses.",
-}
+  @misc{aguiar2025ieligiblenaturallanguage,
+        title={Am I eligible? Natural Language Inference for Clinical Trial Patient Recruitment: the Patient's Point of View}, 
+        author={Mathilde Aguiar and Pierre Zweigenbaum and Nona Naderi},
+        year={2025},
+        eprint={2503.15718},
+        archivePrefix={arXiv},
+        primaryClass={cs.CL},
+        url={https://arxiv.org/abs/2503.15718}, 
+  }
 
 ```