init

.gitattributes

@@ -63,3 +63,8 @@ data/nell.vocab.txt filter=lfs diff=lfs merge=lfs -text
 data/wiki.test.jsonl filter=lfs diff=lfs merge=lfs -text
 data/wiki.train.jsonl filter=lfs diff=lfs merge=lfs -text
 data/wiki.validation.jsonl filter=lfs diff=lfs merge=lfs -text
+data/nell.vocab.clean.txt filter=lfs diff=lfs merge=lfs -text
+data/nell_filter.test.jsonl filter=lfs diff=lfs merge=lfs -text
+data/nell_filter.train.jsonl filter=lfs diff=lfs merge=lfs -text
+data/nell_filter.validation.jsonl filter=lfs diff=lfs merge=lfs -text
+data/nell_filter.vocab.txt filter=lfs diff=lfs merge=lfs -text

README.md

@@ -33,7 +33,7 @@ An example of `test` of `nell` looks as follows.
 }
 ```
 
-## Statistics
+## Statistics on the NELL test split
 
 - Entity Types
 

data/nell.test.jsonl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:134f41e3db58601aba6d3f903c4e243b5b7e846d61eac37e36c68c130415981e
+oid sha256:fa48d27aba4c3b82c2369313fc7a0436103fcb27115efc4159e8b50d988a6fa3
 size 510425

data/nell.train.jsonl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4fd1b662dba39038af6e63cf343152430290528f8a55265194316cf252646712
-size 1997174
+oid sha256:bc4c73fdae5db374da7855e421176dc09462cc72366040d39ce42d7ec333460d
+size 1997185

data/nell.validation.jsonl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6a51f96df896ae77a46e0684f0b10c5d0b055083daec0b19647abb688c57ca39
+oid sha256:c44da469d07e6c45d8bd71cf84b8abc33b0d4ecf41d395c563f055db58ed0919
 size 222265

data/nell.vocab.clean.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:af38d1a58200cf9a3aa0c0d5e76c30202eff02c0cbd353f1c92640fd3fb0d422
+size 884124

data/nell_filter.test.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7080aa24a120c206d15c89bdeb659613f98943e1a777dc0470d2ba220789f537
+size 328006

data/nell_filter.train.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d5c399247378d26aaf06b3d6bc4770eb861805527311b188aba9a9243b45b84b
+size 1303312

data/nell_filter.validation.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:95da57e7d4674775756c8fdd3e262c1aebb094c65e4c0596a0ae0e859bf38c43
+size 190916

data/nell_filter.vocab.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:52489b95fececef828e13cf875c86128ab3b8134c6bdbce7618d809ee59a75f5
+size 694324

fewshot_link_prediction.py

@@ -28,7 +28,7 @@ _CITATION = """
 
 _HOME_PAGE = "https://github.com/asahi417/relbert"
 _URL = f'https://huggingface.co/datasets/relbert/{_NAME}/resolve/main/data'
-_TYPES = ["nell", "wiki"]
+_TYPES = ["nell", "nell_filter", "wiki"]
 _URLS = {i: {
     str(datasets.Split.TRAIN): [f'{_URL}/{i}.train.jsonl'],
     str(datasets.Split.VALIDATION): [f'{_URL}/{i}.validation.jsonl'],

generate_filtered_data.py

@@ -0,0 +1,90 @@
+import json
+
+
+non_entity_types = [
+    'academicfield',
+    'agent',
+    'agriculturalproduct',
+    'amphibian',
+    'animal',
+    'aquarium',
+    'arachnid',
+    'architect',
+    'arthropod',
+    'bakedgood',
+    'bathroomitem',
+    'bedroomitem',
+    'beverage',
+    'bird',
+    'blog',
+    'bodypart',
+    'bone',
+    'candy',
+    'cave',
+    'chemical',
+    'clothing',
+    'coffeedrink',
+    'condiment',
+    'crimeorcharge',
+    'crustacean',
+    'date',
+    'dateliteral',
+    'economicsector',
+    'fish',
+    'food',
+    'fruit',
+    'fungus',
+    'furniture',
+    'grain',
+    'hallwayitem',
+    'hobby',
+    'insect',
+    'invertebrate',
+    'jobposition',
+    'kitchenitem',
+    'landscapefeatures',
+    'legume',
+    'location',
+    'mammal',
+    'meat',
+    'mlsoftware',
+    'mollusk',
+    'month',
+    'nut',
+    'officebuildingroom',
+    'physiologicalcondition',
+    'plant',
+    'politicsissue',
+    'profession',
+    'professionalorganization',
+    'reptile',
+    'room',
+    'sport',
+    'tableitem',
+    'tradeunion',
+    'vegetable',
+    'vehicle',
+    'vertebrate',
+    'weapon',
+    'wine'
+]
+
+full_data = {}
+for s in ["train", "validation", "test"]:
+    with open(f"data/nell.{s}.jsonl") as f:
+        data = [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
+        data = [i for i in data if i['head_type'] not in non_entity_types and i['tail_type'] not in non_entity_types]
+    with open(f"data/nell_filter.{s}.jsonl", "w") as f:
+        f.write('\n'.join([json.dumps(i) for i in data]))
+
+
+with open("data/nell.vocab.txt") as f:
+    types = [i for i in f.read().split('\n')]
+
+with open("data/nell.vocab.clean.txt") as f:
+    vocab = [i for i in f.read().split('\n')]
+vocab_type = [(a, b.split(":")[1]) for a, b in zip(vocab, types) if len(a) > 0 and len(b) > 0 and len(b.split(":")) > 2]
+vocab_new = [a for a, b in vocab_type if b not in non_entity_types]
+with open("data/nell_filter.vocab.txt", 'w') as f:
+    f.write('\n'.join(vocab_new))
+

process.py

@@ -18,7 +18,75 @@ data_dir_nell = "NELL"
 data_dir_wiki = "Wiki"
 os.makedirs("data", exist_ok=True)
 
-short = ['alcs', "uk", "us", "usa"]
+short = ['alcs', "uk", "us", "usa", "npr", "nbc", "bbc", "cnn", "abc", "cbs", "nfl", "mlb", "nba", "nhl", "pga", "ncaa",
+         "wjhu", "pbs", "un"]
+non_entity_types = [
+    'academicfield',
+    'agent',
+    'agriculturalproduct',
+    'amphibian',
+    'animal',
+    'aquarium',
+    'arachnid',
+    'architect',
+    'arthropod',
+    'bakedgood',
+    'bathroomitem',
+    'bedroomitem',
+    'beverage',
+    'bird',
+    'blog',
+    'bodypart',
+    'bone',
+    'candy',
+    'cave',
+    'chemical',
+    'clothing',
+    'coffeedrink',
+    'condiment',
+    'crimeorcharge',
+    'crustacean',
+    'date',
+    'dateliteral',
+    'economicsector',
+    'fish',
+    'food',
+    'fruit',
+    'fungus',
+    'furniture',
+    'grain',
+    'hallwayitem',
+    'hobby',
+    'insect',
+    'invertebrate',
+    'jobposition',
+    'kitchenitem',
+    'landscapefeatures',
+    'legume',
+    'location',
+    'mammal',
+    'meat',
+    'mlsoftware',
+    'mollusk',
+    'month',
+    'nut',
+    'officebuildingroom',
+    'physiologicalcondition',
+    'plant',
+    'politicsissue',
+    'profession',
+    'professionalorganization',
+    'reptile',
+    'room',
+    'sport',
+    'tableitem',
+    'tradeunion',
+    'vegetable',
+    'vehicle',
+    'vertebrate',
+    'weapon',
+    'wine'
+]
 
 
 def clean(token):
@@ -27,9 +95,7 @@ def clean(token):
     token = token.replace("__", "")
     token = re.sub(r"00\d\Z", "", token)
     token = re.sub(r"\An(\d+)", r"\1", token)
-    if _type in ['animal', 'sea_creatures', 'bodypart', "arthropod", "insect", "crustacean", "invertebrate", "arachnid",
-                 "crustacean", "agriculturalproduct", "reptile", "mammal", "amphibian", "sport", "hobby", "vegetable",
-                 "beverage", "fruit", "grain", "coffeedrink", ]:
+    if _type in non_entity_types:
         return token, _type
     new_token = []
     for _t in token.split(" "):
@@ -74,6 +140,9 @@ if __name__ == '__main__':
     vocab = read_vocab(f"{data_dir_nell}/ent2ids")
     with open("data/nell.vocab.txt", 'w') as f:
         f.write("\n".join(vocab))
+    vocab_clean = [clean(i)[0] if len(i.split(":")) > 2 else i for i in vocab]
+    with open("data/nell.vocab.clean.txt", 'w') as f:
+        f.write("\n".join(vocab_clean))
 
     vocab = read_vocab(f"{data_dir_wiki}/ent2ids")
     with open("data/wiki.vocab.txt", 'w') as f: