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import os
import json
import glob
from tqdm import tqdm
from io import StringIO
import pandas as pd
"""
Steps to convert the data into JSON format.
Step-0: Use a python environment where pandas is installed.
Step-1: Download the source file from here: https://figshare.com/articles/dataset/MACCROBAT2018/9764942
Step-2: Unzip the file in put that into a folder (say `data` folder).
All unzipped files will be present here.
* data/MACCROBAT2020/*.txt
* data/MACCROBAT2020/*.ann
Step-3: Use the correct paths and run this file.
"""
def remove_overlapped_ner_tags(ner_details: list[dict]):
"""remove overlapping entities.
Args:
ner_details (List[dict]): a list of dictionary where each dictionary holds
the information of a entity.
NOTE: Priority is given to the entity that is labelled first after sorting all by start index in ascending order.
(i.e. it's end-index is less than other start of other overlapping entity.)
Returns:
list[dict]: updated list (removed item if something was overlapping)
"""
# funtion to remove the overlapping NER-tags
new_ner_details = []
ner_details = sorted(ner_details, key=lambda x: x["start"])
for i, ner_detail in enumerate(ner_details):
if i == 0:
start = ner_detail["start"]
end = ner_detail["end"]
new_ner_details.append(ner_detail)
continue
current_start = ner_detail["start"]
current_end = ner_detail["end"]
if current_start < end:
continue
# update the start and end
start = current_start
end = current_end
new_ner_details.append(ner_detail)
return new_ner_details
def get_ner_details(ann_file):
with open(ann_file, "r") as f:
lines = f.readlines()
lines = [line.strip() for line in lines]
csv_data = "\n".join(lines)
csv_data = StringIO(csv_data)
df = pd.read_csv(csv_data, sep="\t", header=None)
df.columns = ["EntityID", "EntityDetails", "EntityText"]
# print(df.shape)
# remove rows where entity-id start other than `T`
df = df[df["EntityID"].apply(lambda x: str(x).strip().startswith("T"))]
# remove the rows which contains the ";" in the `EntityDetails`
df = df[df["EntityDetails"].apply(lambda x: ";" not in str(x))]
# drop where None is present
df.dropna(axis=1, inplace=True)
ner_info = []
for i, row in df.iterrows():
text = row["EntityText"]
details = row["EntityDetails"]
try:
ner_tag, start, end = details.split(" ")
except:
print(ann_file)
print(details)
start = int(float(start))
end = int(float(end))
ner_info.append({"text": text, "label": ner_tag.upper(), "start": start, "end": end})
# remove the overlapping entities
ner_info = remove_overlapped_ner_tags(ner_details=ner_info)
# print(ner_info)
return ner_info
def main(input_path: str = "data/MACCROBAT2020", output_path: str = "data/MACCROBAT2020-V2.json"):
txt_files = glob.glob(os.path.join(input_path, "*.txt"))
txt_files.sort()
ner_data = {"data": [], "verson": "MACCROBAT-V2 (https://figshare.com/articles/dataset/MACCROBAT2018/9764942)"}
for txt_file in tqdm(txt_files, desc="Extracting data..."):
with open(txt_file, "r") as f:
full_text = f.read()
a = txt_file.replace(".txt", ".ann")
ner_info = get_ner_details(a)
data = {"full_text": full_text, "ner_info": ner_info}
ner_data["data"].append(data)
ALL_NER_LABLES = set()
for details in tqdm(ner_data["data"], desc="Splitting into tokens..."):
text = details["full_text"]
ner_details = details["ner_info"]
tokens = []
ner_labels = []
start = 0
for ner_detail in ner_details:
ner_start = ner_detail["start"]
ner_end = ner_detail["end"]
before_ner_token = text[start:ner_start]
ner_token = text[ner_start:ner_end]
tokens.append(before_ner_token)
ner_labels.append("O")
tokens.append(ner_token)
ner_labels.append(f'B-{ner_detail["label"]}')
ALL_NER_LABLES.add(f'B-{ner_detail["label"]}')
ALL_NER_LABLES.add(f'I-{ner_detail["label"]}')
start = ner_end
if len(text) >= start:
ner_labels.append("O")
tokens.append(text[start:])
assert len(tokens) == len(ner_labels)
details["tokens"] = tokens
details["ner_labels"] = ner_labels
ner_data["all_ner_labels"] = sorted(list(ALL_NER_LABLES), key=lambda x: x.split("-")[-1])
label_2_index = {k: i for i, k in enumerate(ner_data["all_ner_labels"])}
index_2_label = {v: k for k, v in label_2_index.items()}
ner_data["label_2_index"] = label_2_index
ner_data["index_2_label"] = index_2_label
for details in tqdm(ner_data["data"], desc="label2index..."):
ner_labels = details["ner_labels"]
ner_labels_ids = []
for ner in ner_labels:
ner_labels_ids.append(label_2_index.get(ner))
details["ner_labels"] = ner_labels_ids
with open(output_path, "w") as f:
json.dump(ner_data, f, indent=4)
if __name__ == "__main__":
input_path: str = "data/MACCROBAT2020"
output_path: str = "data/MACCROBAT2020-V2.json"
main(input_path=input_path, output_path=output_path)
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