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uwsd-bert.py
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uwsd-bert.py
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# -*- coding: utf-8 -*-
"""
[Martinez-Gil2023b] Context-Aware Semantic Similarity Measurement for Unsupervised Word Sense Disambiguation, arXiv preprint arXiv:2305.03520, 2023
@author: Jorge Martinez-Gil
"""
#BERT sentence transformer
from sentence_similarity import sentence_similarity
import os
import json
# Define the path to the parent directory containing the folders
parent_dir = os.getcwd() + "\\CoarseWSD-20"
# mpnet-base-v2, all-MiniLM-L12-v2, all-MiniLM-L6-v2, paraphrase-albert-small-v2, paraphrase-MiniLM-L3-v2, distilroberta-v1
model=sentence_similarity(model_name='all-MiniLM-L6-v2',embedding_type='sentence_embedding')
def find_token_position(token, lst):
for i, s in enumerate(lst):
if token in s:
return i
return 0 # Token not found in any string
def calculate(synonyms, word, context):
fw = 'null'
maximum = 9999
for i in range(len(synonyms)):
cons = synonyms[int(i)]
cons = cons.replace('_', ' ')
cons = cons.replace('(', '')
cons = cons.replace(')', '')
tokens_to_check = cons.split ()
for token in tokens_to_check:
if token.lower () == word.lower():
cons = cons.replace(token, "")
if word.lower() != cons.lower():
source = context.replace(word, cons)
target = context
result = 1-model.get_score(source,target,metric="cosine")
# print ('Comparing ' + source + ' <-> ' + target + ' ' + str(result))
if result < maximum:
fw = cons
maximum = result
return fw
overall_nums = 0
overall_res = 0
overall_baseline = 0
for folder in os.listdir(parent_dir):
synonyms = []
nums = []
data = []
results = []
# Define the path to the folder
folder_path = os.path.join(parent_dir, folder)
# Define the path to the file
file_path = os.path.join(folder_path, 'classes_map.txt')
with open(file_path, "r") as f:
# Load the data from the file using the json module
dato = json.load(f)
# Access the values in the data dictionary using their keys
for key in dato.keys():
synonyms.append (dato[key])
file_path = os.path.join(folder_path, 'test.gold.txt')
with open(file_path, 'r') as f:
# Read the lines and remove any whitespace characters
lines = [line.strip() for line in f.readlines()]
# Convert the lines to integers and store them in a list
nums = [int(line) for line in lines]
file_path = os.path.join(folder_path, 'test.data.txt')
with open(file_path, 'r', encoding="utf8") as f:
# Read the lines and split them into the number and text sections
lines = [line.strip().split('\t') for line in f.readlines()]
# Create a list of dictionaries with keys 'number' and 'text'
data = [{'number': int(line[0]), 'text': line[1]} for line in lines]
for item in data:
r = 0
tokens = item['text'].split()
nth_token = tokens[item['number']]
fr = calculate (synonyms, nth_token, item['text'])
fr = fr.replace(" ", "")
r = find_token_position(str(fr), list(synonyms))
#print (str(fr) + '>>>' + str(synonyms) + '>>>' + str(r))
results.append(r)
res = sum(x == y for x, y in zip(nums, results))
f = folder_path.split ()
print (str(f[-1]) + ' result : ' + str(res) + ' in percentage: ' + str(res/len(nums)))
overall_nums = overall_nums + len(nums)
overall_res = overall_res + res
count_dict = {}
for item in nums:
if item in count_dict:
count_dict[item] += 1
else:
count_dict[item] = 1
print(count_dict)
max_value = max(count_dict.values())
overall_baseline = overall_baseline + max_value
print ('Final result : ' + str(overall_res) + ' in percentage: ' + str(overall_res/overall_nums))
print ('Baseline result : ' + str(overall_baseline) + ' in percentage: ' + str(overall_baseline/overall_nums))