Update README.md

README.md

@@ -39,10 +39,9 @@ To access concept–pictograph pairs, please refer to [this repository](https://
 
 ## Dataset
 
-The dataset includes two files:
+The dataset includes one file:
 
 - **`translations.csv`**: French sentences with their human translations into the target languages.
-- **`paraphrases.csv`**: French sentences (same as in translations.csv) with French paraphrases, semantic glosses and diagnostic domain(s).
 
 ### translation.csv
 
@@ -103,93 +102,6 @@ The following table summarises the data by language. Each row corresponds to a *
 
 ---
 
-### paraphrases.csv
-
-The dataset consists of multiple French paraphrases of the source sentences. French variations created by grammar-based synthetic data generation, which introduces syntactic variation while preserving meaning. Note: these automatically generated sentences are not always fully grammatical.
-
-#### Data Structure
-
-Each row in the dataset represents a **paraphrase** of a French source sentence.
-
-- `sentence_id`: Unique identifier for the source sentence (shared across languages/variants)
-- `src_text`: Original French sentence
-- `paraphrase`: Generated paraphrase of the source. May not always be fully grammatical.
-- `semantic_gloss`: Semantic representation: pipe-separated sequence of concepts using concept names (UMLS + custom concepts).
-- `domains`: Diagnostic domains in which the sentence is used (multiple values separated by `\`).
-- `n_domains`: Number of diagnostic domains associated with the sentence. |
-- `CUI_semantic_gloss`: Semantic representation: pipe-separated sequence of concepts using CUIs for UMLS concepts and names for custom concepts (aligned 1:1 with `semantic_gloss`)
-
-#### Diagnostic domains
-
-- Medical consultations
-- Questions and instructions (e.g., symptom checks, treatment directives)
-- Categories by body region (e.g., head, chest, abdomen)
-
-Each sentence is associated with one or more diagnostic domains, reflecting the **body system** or **clinical situation** covered by the sentence:
-
-- **`checkup`**  
-  General health checkups and preventive care (e.g., routine questions about lifestyle, vaccination, or screening).
-
-- **`chest`**  
-  Respiratory and thoracic conditions (e.g., cough, shortness of breath, chest pain).
-
-- **`covid`**  
-  Sentences specifically related to COVID-19 (e.g., symptoms like fever, loss of smell, quarantine and testing instructions).
-
-- **`dermato`**  
-  Dermatology: skin, hair, and nail conditions (e.g., rashes, infections, wounds, allergies).
-
-- **`drogue`**  
-  Substance use and drug-related issues (e.g., questions about alcohol, tobacco, or illicit drug consumption).
-
-- **`merged_hea_orl_sui`**  
-  Combined domain including the following related domains:
-
-  - **HEA** = Head
-  - **ORL** = Oto-Rhino-Laryngology (ENT: ear, nose, throat)
-  - **SUI** = Suivi (follow-up)
-
-- **`merged_uri_col_abd_anu_sui`**  
-  Combined domain including the following related domains:
-
-  - **URI** = Urinary tract
-  - **COL** = Colon
-  - **ABD** = Abdomen
-  - **ANU** = Anus/rectum
-  - **SUI** = Suivi (follow-up)
-
-- **`suivi`**  
-  Follow-up consultations (e.g., treatment monitoring, recovery questions, long-term care instructions).
-
-- **`traumatologie`**
-  Traumatology: accidents and injuries (e.g., fractures, wounds, burns, emergency trauma-related questions).
-
----
-
-#### Distribution
-
-The number of rows per diagnostic domain is shown below:
-
-| Domain                     |  Count |
-| -------------------------- | -----: |
-| merged_uri_col_abd_anu_sui | 397225 |
-| merged_hea_orl_sui         | 251558 |
-| chest                      | 190815 |
-| checkup                    | 150490 |
-| dermato                    | 121151 |
-| traumatologie              | 104698 |
-| suivi                      |  45722 |
-| drogue                     |  39561 |
-| covid                      |   6503 |
-
-General statistics of the dataset are given below:
-
-| Metric                           |     Value |
-| -------------------------------- | --------: |
-| Total rows                       | 1,104,502 |
-| Unique paraphrases               | 1,087,150 |
-| Rows with empty `semantic_gloss` |   141,858 |
-
 ## Example
 
 ```text
@@ -254,31 +166,6 @@ If you use the translations, please cite:
 }
 ```
 
-If you use the UMLS glosses, please cite:
-
-```
-@inproceedings{gerlach-etal-2024-concept,
-    title = "A Concept Based Approach for Translation of Medical Dialogues into Pictographs",
-    author = "Gerlach, Johanna  and
-      Bouillon, Pierrette  and
-      Mutal, Jonathan  and
-      Spechbach, Herv{\'e}",
-    editor = "Calzolari, Nicoletta  and
-      Kan, Min-Yen  and
-      Hoste, Veronique  and
-      Lenci, Alessandro  and
-      Sakti, Sakriani  and
-      Xue, Nianwen",
-    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
-    month = may,
-    year = "2024",
-    address = "Torino, Italia",
-    publisher = "ELRA and ICCL",
-    url = "https://aclanthology.org/2024.lrec-main.21/",
-    pages = "233--24"
-}
-```
-
 ---
 
 ## Acknowledgments