def main():
nlp = spacy.load("en_core_web_lg")
directory = "/home/harsh/Downloads/data/ner-eval-collection-master/plainTextFiles/"
results = []
for i in range(0, 128):
filename = directory + str(i) + ".txt"
file = open(filename, "r")
file_content = file.read()
file_content = file_content.split("<delim>")
article_text = file_content[0]
doc = nlp(article_text)
if file_content[1]:
file_content[1].strip()
mse_text = file_content[1].replace("[", "").replace("]", "").replace("\n", "")
mse_text = mse_text.split(",")
if file_content[2]:
file_content[2].strip()
lse_text = file_content[2].replace("[", "").replace("]", "").replace("\n", "")
lse_text = lse_text.split(",")
article_result = Result(i, article_text, mse_text, lse_text)
frequency_analysis(doc, article_result)
results.append(article_result)
# print(article_result.toString())
graph = Graphs(results)
graph.graph2('frequency_analysis')
