Datasets:
Tasks:
Text Classification
Sub-tasks:
topic-classification
Languages:
English
Size:
100K<n<1M
License:
| # Parses raw semcor into csv files | |
| import pandas as pd | |
| import os | |
| from bs4 import BeautifulSoup | |
| def process_split(split_name, parent_path="semcor3.0"): | |
| data = [] | |
| for file in os.listdir(os.path.join(parent_path, split_name, "tagfiles")): | |
| file_path = os.path.join(parent_path, split_name, "tagfiles", file) | |
| with open(file_path, "r") as f: | |
| raw_file = f.read() | |
| parsed_file = BeautifulSoup(raw_file, "html.parser") | |
| for p in parsed_file.contextfile.context.find_all("p"): | |
| pnum = p.get("pnum") | |
| for s in p.find_all("s"): | |
| snum = s.get("snum") | |
| for child in s.find_all(text=False): | |
| child_data = { | |
| "tagfile": file, | |
| "pnum": pnum, | |
| "snum": snum, | |
| "tag": child.name, | |
| "lemma": child.get("lemma"), | |
| "lexsn": child.get("lexsn"), | |
| "wnsn": child.get("wnsn"), | |
| "value": child.string, | |
| "cmd": child.get("cmd"), | |
| "dc": child.get("dc"), | |
| "ot": child.get("ot"), | |
| "pn": child.get("pn"), | |
| "pos": child.get("pos"), | |
| "rdf": child.get("rdf"), | |
| "sep": child.get("sep"), | |
| } | |
| data.append(child_data) | |
| types_dict = { | |
| "tagfile": str, | |
| "pnum": int, | |
| "snum": int, | |
| "tag": str, | |
| "lemma": str, | |
| "lexsn": str, | |
| "wnsn": str, | |
| "value": str, | |
| "cmd": str, | |
| "dc": str, | |
| "ot": str, | |
| "pn": str, | |
| "pos": str, | |
| "rdf": str, | |
| "sep": str, | |
| } | |
| df = pd.DataFrame(data) | |
| df = df.astype(types_dict) | |
| return df | |
| if __name__ == "__main__": | |
| for split in ["brown1", "brown2", "brownv"]: | |
| print(f"processing split {split}") | |
| df = process_split(split) | |
| df.to_csv(f"data/{split}-00000-of-00001.csv", index=False) | |
| print("Done. Saved to disk.") | |