Update causenet_wiki.py

causenet_wiki.py

@@ -0,0 +1,97 @@
+import csv
+import json
+import datasets
+from datasets.tasks import TextClassification
+
+
+_CITATION = """\
+@inproceedings{heindorf2020causenet,
+  author    = {Stefan Heindorf and
+               Yan Scholten and
+               Henning Wachsmuth and
+               Axel-Cyrille Ngonga Ngomo and
+               Martin Potthast},
+  title     = CauseNet: Towards a Causality Graph Extracted from the Web,
+  booktitle = CIKM,
+  publisher = ACM,
+  year      = 2020
+}
+"""
+
+_DESCRIPTION = """\
+Crawled Wikipedia Data from CIKM 2020 paper 
+'CauseNet: Towards a Causality Graph Extracted from the Web.' 
+"""
+_URL = "https://github.com/causenet-org/CIKM-20"
+
+# use dl=1 to force browser to download data instead of displaying it
+_TRAIN_DOWNLOAD_URL = "https://groups.uni-paderborn.de/wdqa/causenet/causality-graphs/extraction/wikipedia/wikipedia-extraction.tsv"
+
+
+class CauseNetWikiCorpus(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                "cause_word": datasets.Value("string"), 
+                "cause_id": datasets.Value("int64"),
+                "effect_word": datasets.Value("string"),
+                "effect_id": datasets.Value("int64"),
+                "pattern": datasets.Value("string"), 
+                "sentence": datasets.Value("string"), 
+                "dependencies": datasets.Value("string")
+                }
+            ),
+            homepage=_URL,
+            citation=_CITATION,
+            task_templates=None
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path})
+        ]
+
+
+    def is_valid_article(self, title):
+        forbidden_title_parts = ['Wikipedia:', 'Template:', 'File:',
+                                'Portal:', 'Category:', 'Draft:',
+                                'List of', 'disambiguation']
+
+        contains_forbidden_title_part = False
+        for forbidden_title_part in forbidden_title_parts:
+            if forbidden_title_part in title:
+                contains_forbidden_title_part = True
+                break
+
+        return not contains_forbidden_title_part
+
+
+    def _generate_examples(self, filepath):
+        """
+        Generate examples.
+        We are reading csv files with the following columns: sentenceID	| gold_label | sentence. 
+        """
+        for id_, line in enumerate(open(filepath, encoding="utf-8")):
+            parts = line.strip().split('\t')
+            if parts[0] != 'wikipedia_sentence':
+                continue
+            assert len(parts) == 11
+
+            if not self.is_valid_article(parts[2]):
+                continue
+
+            for match in json.loads(parts[10]):
+                sentence_data = {
+                    "cause_word": match['Cause'][0],
+                    "cause_id": match['Cause'][1],
+                    "effect_word": match['Effect'][0],
+                    "effect_id": match['Effect'][1],
+                    "pattern": match['Pattern'],
+                    "sentence": json.loads(parts[7]),
+                    "dependencies": json.loads(parts[9])
+                }
+            
+            yield id_, sentence_data