Add domain information where it exists

slo_thesaurus.py

@@ -1,11 +1,10 @@
 """ An automatically created Slovene thesaurus. """
 import logging
-import xml.etree.ElementTree as ET
 import os
+import xml.etree.ElementTree as ET
 
 import datasets
 
-
 _CITATION = """\
 @article{krek2017translation,
   title={From translation equivalents to synonyms: creation of a Slovene thesaurus using word co-occurrence network analysis},
@@ -47,14 +46,16 @@ class SloThesaurus(datasets.GeneratorBasedBuilder):
                     {
                         "id_words": datasets.Sequence(datasets.Value("string")),
                         "words": datasets.Sequence(datasets.Value("string")),
-                        "scores": datasets.Sequence(datasets.Value("float32"))
+                        "scores": datasets.Sequence(datasets.Value("float32")),
+                        "domains": datasets.Sequence(datasets.Sequence(datasets.Value("string")))
                     }
                 ],
                 "groups_near": [
                     {
                         "id_words": datasets.Sequence(datasets.Value("string")),
                         "words": datasets.Sequence(datasets.Value("string")),
-                        "scores": datasets.Sequence(datasets.Value("float32"))
+                        "scores": datasets.Sequence(datasets.Value("float32")),
+                        "domains": datasets.Sequence(datasets.Sequence(datasets.Value("string")))
                     }
                 ]
             }
@@ -95,13 +96,21 @@ class SloThesaurus(datasets.GeneratorBasedBuilder):
             core_groups = curr_entry.find("groups_core")
             if core_groups is not None:
                 for idx_group, core_group in enumerate(core_groups.iterfind("group"), start=0):
-                    parsed_group = {"id_words": [], "words": [], "scores": []}
+                    parsed_group = {"id_words": [], "words": [], "scores": [], "domains": []}
                     all_candidates = core_group.iterfind("candidate")
                     for candidate in all_candidates:
                         candidate_s = candidate.find("s")
+                        candidate_domains = candidate.find("labels")
+                        if candidate_domains is not None:
+                            candidate_domains = list(map(lambda candidate_el: candidate_el.text.strip(),
+                                                         candidate_domains.findall("la")))
+                        else:
+                            candidate_domains = []
+
                         parsed_group["id_words"].append(candidate_s.attrib["id"])
                         parsed_group["words"].append(candidate_s.text.strip())
                         parsed_group["scores"].append(float(candidate.attrib["score"]))
+                        parsed_group["domains"].append(candidate_domains)
 
                     all_core_groups.append(parsed_group)
 
@@ -109,13 +118,21 @@ class SloThesaurus(datasets.GeneratorBasedBuilder):
             near_groups = curr_entry.find("groups_near")
             if near_groups is not None:
                 for idx_group, core_group in enumerate(near_groups.iterfind("group"), start=0):
-                    parsed_group = {"id_words": [], "words": [], "scores": []}
+                    parsed_group = {"id_words": [], "words": [], "scores": [], "domains": []}
                     all_candidates = core_group.iterfind("candidate")
                     for candidate in all_candidates:
                         candidate_s = candidate.find("s")
+                        candidate_domains = candidate.find("labels")
+                        if candidate_domains is not None:
+                            candidate_domains = list(map(lambda candidate_el: candidate_el.text.strip(),
+                                                         candidate_domains.findall("la")))
+                        else:
+                            candidate_domains = []
+
                         parsed_group["id_words"].append(candidate_s.attrib["id"])
                         parsed_group["words"].append(candidate_s.text.strip())
                         parsed_group["scores"].append(float(candidate.attrib["score"]))
+                        parsed_group["domains"].append(candidate_domains)
 
                     all_near_groups.append(parsed_group)