diff --git a/hospital-affiliations/aff_hosp.py b/hospital-affiliations/aff_hosp.py
index c741bad..525ecff 100755
--- a/hospital-affiliations/aff_hosp.py
+++ b/hospital-affiliations/aff_hosp.py
@@ -1,10 +1,12 @@
 from numpy import column_stack
 import pandas as pd
-pd.options.mode.chained_assignment = None  
 from nltk.tokenize import word_tokenize
 from fuzzywuzzy import fuzz
 import sys
 import json
+import unicodedata
+import re
+pd.options.mode.chained_assignment = None  
 
 ## MODIFICATION DU FICHIER EXCEL
 # On regarde si la liste des termes acronymes choisis est présente dans les affiliations
@@ -16,23 +18,25 @@
 # Il faut également que l'affiliation qu'on veut homogénéiser ait un terme de la liste
 
 
-#Correction des villes
-def correction(texte) :
+#correction of the cities : remove the department numbers and transformation into acronyms
+def remove_department_numbers_and_acronyms(city_name) :
+    # department numbers: Grenoble - 38 => Grenoble
     sep = ' -'
-    stripped = texte.split(sep, 1)[0]
+    stripped = city_name.split(sep, 1)[0]
     stripped = stripped.replace("-"," ")
-    stripped = stripped.replace("ç","c")
+
+    # acronyms
     stripped = stripped.replace("Saint","St")
-    stripped = stripped.replace("é","e")
-    stripped = stripped.replace("è","e")
-    stripped = stripped.replace("É","E")
-    stripped = stripped.replace("â","a")
-    stripped = stripped.replace("ê","e")
-    stripped = stripped.replace("î","i")
-    stripped = stripped.replace("à","a")
+    stripped = stripped.replace("Mont","Mt")
 
     return stripped
 
+# correction of the cities : remove accents
+def remove_accents(city_name) :
+    normalized_text = unicodedata.normalize("NFD", city_name)
+    text_with_no_accent = re.sub("[\u0300-\u036f]", '', normalized_text)
+    return text_with_no_accent
+
 # On repère/marque les lignes dont l'affiliation contient la ville
 def aff_ville(ville,texte):
     if ville.lower() in texte.lower() :
@@ -55,7 +59,8 @@
         if ac in texte.lower():
             # APPEL DES FONCTIONS
             df['Terme Affiliation'] = df["Affiliation"].apply(terme_aff,liste=acro)
-            df['Ville_canonique_Dpt'] = df['Ville_canonique_Dpt'].apply(correction)
+            df['Ville_canonique_Dpt'] = df['Ville_canonique_Dpt'].apply(remove_department_numbers_and_acronyms)
+            df['Ville_canonique_Dpt'] = df['Ville_canonique_Dpt'].apply(remove_accents)
             if len(df[df['Terme Affiliation'] == True]) != 0:
                 df2 = df[df['Terme Affiliation'] == True]
                 df2["Ville_présente"] = df2['Ville_canonique_Dpt'].apply(aff_ville,texte=texte)
diff --git a/hospital-affiliations/input_data.txt b/hospital-affiliations/input_data.txt
index e375b72..9a74acb 100644
--- a/hospital-affiliations/input_data.txt
+++ b/hospital-affiliations/input_data.txt
@@ -2,4 +2,4 @@
 {"id" :"4","value": "Hop Necker Enfants Malad, Gen Pediat Dept, Paris, France"}
 {"id" :"5","value": "Bergonie Inst, Dept Radiat Oncol, Bordeaux, France"}
 {"id" :"6","value": "CHU Besancon, Serv Neurol, 2 Blvd Fleming, F-25030 Besancon, France"}
-{"id" :"7","value": "Univ Bordeaux, INCIA, CNRS UMR 5287, Equipe Neuropsychopharmacol Addict, BP31,146 Rue Leo Saignat, F-33076 Bordeaux, France"}
\ No newline at end of file
+{"id" :"7","value": "CHU St Etienne, Ave Albert Raimond, F-42055 St Etienne, France"}
\ No newline at end of file