Added gigafida fix for multiple senses

scripts/create_xml.py

@@ -1181,7 +1181,7 @@ def write_xml(headword_category, collection_ssj, collection_gigafida, RF, mongo,
                     for sentence_example in headword_pattern_dict['gf']['sentence_examples']:
                         exampleContainer = lxml.SubElement(exampleContainerList, 'exampleContainer')
                         # corpusExample = lxml.SubElement(exampleContainer, 'corpusExample')
-                        exampleContainer.append(sentence_example)
+                        exampleContainer.append(copy.deepcopy(sentence_example))
         with lxml.xmlfile(os.path.join(args.outdir, 'VS10_' + headword_text + '_' + corpus_name + '.xml'),
                               encoding='utf-8') as xf:
             xf.write(dictionary, pretty_print=True)

scripts/form_csv.py

@@ -2,7 +2,7 @@ import argparse
 import csv
 import os
 
-from lxml import etree
+from lxml import etree, objectify, html
 
 
 def write_general_statistics(path, out_list):
@@ -11,7 +11,18 @@ def write_general_statistics(path, out_list):
     with open(path, 'w') as csvfile:
         writer = csv.writer(csvfile, delimiter='\t',
                                 quotechar='"')
-        writer.writerow(['semanticRole', 'valency_pattern_ratio', 'valency_sentence_ratio'])
+        writer.writerow(['Semantic role', 'Valency pattern ratio', 'Valency sentence ratio'])
+        for line in out_list:
+            writer.writerow(line)
+
+
+def write_statistics(path, out_list):
+    if len(out_list) == 0:
+        return
+    with open(path, 'w') as csvfile:
+        writer = csv.writer(csvfile, delimiter='\t',
+                            quotechar='"')
+        writer.writerow(['Valency pattern id', 'Frequency all GF', 'Semantic role', 'Pattern representation', 'Corpus example'])
         for line in out_list:
             writer.writerow(line)
 
@@ -48,15 +59,53 @@ def main(args):
             if ssj_pattern is not None and ssj_sentence is not None:
                 ssj_output.append([semRole, ssj_pattern, ssj_sentence])
 
+        print(file)
+
+        analyze_output = []
         for elem in tree.iter('valencyPattern'):
             valency_pattern_id = elem.attrib['id']
-            measure = None
+
-            for measure_el in elem.find('measure'):
+            # get frequency
+            measure = ''
+            for measure_el in elem.find('measureList').findall('measure'):
                 if measure_el.attrib['source'] == 'Gigafida 2.0':
                     measure = measure_el.text
 
+            # get semantic roles
+            semantic_roles_list = []
+            for semantic_rol_con in elem.find('semanticRoleContainerList').findall('semanticRoleContainer'):
+                semantic_roles_list.append(semantic_rol_con.find('semanticRole').text)
+            semantic_roles = '_'.join(semantic_roles_list)
+
+            # pattern representation
+            pattern_representation = elem.find('patternRepresentation').text
+
+            # corpus example
+            if elem.find('exampleContainerList') is not None and elem.find('exampleContainerList').find('exampleContainer') is not None and elem.find('exampleContainerList').find('exampleContainer').find('corpusExample') is not None:
+                corpus_example_text = html.tostring(elem.find('exampleContainerList').find('exampleContainer').find('corpusExample'), encoding='unicode')
+
+            else:
+                continue
+
+            # ugly postprocessing to remove xmlns:xsi=... duh..
+            root = etree.fromstring(corpus_example_text)
+
+            # Remove namespace prefixes
+            for elem in root.getiterator():
+                elem.tag = etree.QName(elem).localname
+            # Remove unused namespace declarations
+            etree.cleanup_namespaces(root)
+
+            corpus_example = etree.tostring(root, encoding='unicode')
+
+            print(f"Valency pattern {valency_pattern_id}")
+
+
+            analyze_output.append([valency_pattern_id, measure, semantic_roles, pattern_representation, corpus_example])
+
         write_general_statistics(os.path.join(args.output, headword + '_gf_stats.tsv'), gf_output)
         write_general_statistics(os.path.join(args.output, headword + '_ssj_stats.tsv'), ssj_output)
+        write_statistics(os.path.join(args.output, headword + '_patterns.tsv'), analyze_output)
 
 if __name__ == '__main__':
     arg_parser = argparse.ArgumentParser(description='Export and validate collocation data from DDD database.')