diff --git a/data/lists/not_names.txt b/data/lists/not_names.txt
index 044045dcdb6931137c5a565ec16bc5e6b39e5936..1a44b305e7d8e5267e6140416581f5b397009058 100644
--- a/data/lists/not_names.txt
+++ b/data/lists/not_names.txt
@@ -51,6 +51,7 @@ StGallen
Stimmen
Stimme
stimmt
+Studien
tischen
Tunnel
Ueber
diff --git a/data/lists/wrongly_identified_speakers.txt b/data/lists/wrongly_identified_speakers.txt
index db6bb1a1de5c566d89a730c99ba6aa95b098cef6..c46c3fe19a88256f573d41983299317e502749b8 100644
--- a/data/lists/wrongly_identified_speakers.txt
+++ b/data/lists/wrongly_identified_speakers.txt
@@ -68,6 +68,7 @@ term very similar to one name is actually another name
------------------------------------------------------
1925/20029863: ganz --> finds Lanz, there is a Ganz
1971/20000630 and others: Schweizer --> finds Schneider, there is a Schweizer
+1951/20035112: Schweizer --> finds Schwizer
term is a name
@@ -89,6 +90,10 @@ person has entry date 29.11.71 but is not yet active (presumably):
1971/20000726: one Muheim starts 29.11.71, discussion is on 8.12.71 --> finds two!
+Firstname before LastName
+-------------------------
+1971/20000592: Simon Kohler rapporteur
+
two people with same last name and same citizenship
---------------------------------------------------
1951/20034993: Eggenberger Grabs
diff --git a/src/python/utils_annot.py b/src/python/utils_annot.py
index ceedfc9b8d8c44d20972e5a6d29267f66efa0bb6..8572e209b340f644e120b0fe377fd1885a5ee240 100644
--- a/src/python/utils_annot.py
+++ b/src/python/utils_annot.py
@@ -467,7 +467,7 @@ def find_names(list_oi, list_roles, list_roles_ext, df_names, list_notnames, str
# TODO: how to handle for people mentioned in text???
elif name_type in ['canton']:
- list_temp = list(df_names.loc[(df_names['nameType']==name_type) & (df_names['shortName']==term)].iloc[:, df_names.columns.get_loc('uniqueIndex')])
+ list_temp = list(df_names.loc[(df_names['shortName']==term)].iloc[:, df_names.columns.get_loc('uniqueIndex')])
str_completeName = term + ' (CANTON MISSING)'
print(list_temp, str_completeName)
@@ -598,8 +598,8 @@ def find_names(list_oi, list_roles, list_roles_ext, df_names, list_notnames, str
# if person was not uniquely identified, check for misspellings
if not canton_type:
# look for similar names based on (normalized) Damerau-Levenshtein distance
- # only look at cantonname, citizenship and additionalinfo
- list_cantons_approx = [list_cantons[i] for i in (0, 2, 4)]
+ # only look at cantonname, citizenship, firstname and additionalinfo
+ list_cantons_approx = [list_cantons[i] for i in (0, 2, 3, 4)]
for list_, type_ in list_cantons_approx:
term_approx = get_approximate_term(term, np.array(list_))
if term_approx:
@@ -613,7 +613,7 @@ def find_names(list_oi, list_roles, list_roles_ext, df_names, list_notnames, str
# get rid of CANTON MISSING
str_name = str_name.split(' ')[0]
- df_temp = get_df_temp_canton(df_names, str_name, str_council)
+ df_temp = get_df_temp(df_names, str_name, str_council)
# extract uniqueID
# if Citizenship, get list of cities and compare each to term
if canton_type == 'Citizenship':
@@ -622,20 +622,20 @@ def find_names(list_oi, list_roles, list_roles_ext, df_names, list_notnames, str
str_citizenship = ''
if len(list_cities) == 1:
str_citizenship = list_cities[0]
- list_temp = list(df_temp.loc[(df_temp['nameType']==name_type) & (df_temp['shortName']==str_name) & (df_temp[canton_type]==str_citizenship)].iloc[:, df_temp.columns.get_loc('uniqueIndex')])
- str_completeName = df_temp['completeName'].loc[(df_temp['nameType']==name_type) & (df_temp['shortName']==str_name) & (df_temp[canton_type]==str_citizenship)].iloc[0]
+ list_temp = list(df_temp.loc[(df_temp['shortName']==str_name) & (df_temp[canton_type]==str_citizenship)].iloc[:, df_temp.columns.get_loc('uniqueIndex')])
+ str_completeName = df_temp['completeName'].loc[(df_temp['shortName']==str_name) & (df_temp[canton_type]==str_citizenship)].iloc[0]
elif len(list_cities) > 1:
print('found more than one person with citizenship', str_canton, str_name, list_cities)
# TODO what happens with these:?
- list_temp = list(df_names.loc[(df_names['nameType']==name_type) & (df_names['shortName']==str_name)].iloc[:, df_names.columns.get_loc('uniqueIndex')])
+ list_temp = list(df_names.loc[(df_names['shortName']==str_name)].iloc[:, df_names.columns.get_loc('uniqueIndex')])
str_completeName = str_name + ' (CANTON MISSING)'
else:
print('found no person with citizenship', str_canton, str_name, list_cities)
else:
- list_temp = list(df_temp.loc[(df_temp['nameType']==name_type) & (df_temp['shortName']==str_name) & (df_temp[canton_type]==str_canton)].iloc[:, df_temp.columns.get_loc('uniqueIndex')])
- str_completeName = df_temp['completeName'].loc[(df_temp['nameType']==name_type) & (df_temp['shortName']==str_name) & (df_temp[canton_type]==str_canton)].iloc[0]
+ list_temp = list(df_temp.loc[(df_temp['shortName']==str_name) & (df_temp[canton_type]==str_canton)].iloc[:, df_temp.columns.get_loc('uniqueIndex')])
+ str_completeName = df_temp['completeName'].loc[(df_temp['shortName']==str_name) & (df_temp[canton_type]==str_canton)].iloc[0]
print(list_temp, list_uniqueID, str_completeName)
@@ -799,12 +799,12 @@ def label_language(XML_new, ind_p, ind_t, aux_dict_l):
def get_cities(list_citizenship):
return [city[:-5] for item in list_citizenship for city in item.split(',')]
-def get_df_temp_canton(df_names, str_name, str_council):
+def get_df_temp(df_names, str_name, str_council):
if str_council in ['Nationalrat', 'Ständerat']:
- df_temp = df_names.loc[(df_names['nameType']=='canton') & (df_names['shortName']==str_name) & (df_names['CouncilName']==str_council)]
+ df_temp = df_names.loc[(df_names['shortName']==str_name) & (df_names['CouncilName']==str_council)]
else:
- df_temp = df_names.loc[(df_names['nameType']=='canton') & (df_names['shortName']==str_name)]
+ df_temp = df_names.loc[(df_names['shortName']==str_name)]
return df_temp
@@ -819,7 +819,7 @@ def get_list_cantons(df_names, str_name, str_council = ''):
str_additionalInfo = 'additionalInfo'
# get dataframe
- df_temp = get_df_temp_canton(df_names, str_name, str_council)
+ df_temp = get_df_temp(df_names, str_name, str_council)
# list of cantons
list_cantonname = list(df_temp[str_CantonName])