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FAQs for MSE #35

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FAQs for MSE #35

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9 changes: 6 additions & 3 deletions asf_public_discourse_home_decarbonisation/pipeline/faqs_identification/extract_questions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,6 +33,7 @@
import os
from asf_public_discourse_home_decarbonisation import PROJECT_DIR
import logging
import sys

logger = logging.getLogger(__name__)
nltk.download("punkt")
Expand Down Expand Up @@ -139,6 +140,11 @@ def process_category_data(
"""
# Filter the data by post type
category_dataframe = filter_data_by_post_type(category_dataframe, post_type)

# Remove URLs
category_dataframe["text"] = category_dataframe["text"].apply(remove_urls)
category_dataframe = process_abbreviations(category_dataframe)

# Convert all text to strings to avoid type errors
category_dataframe["text"] = category_dataframe["text"].astype(str)
category_dataframe["sentences"] = category_dataframe["text"].apply(sent_tokenize)
Expand Down Expand Up @@ -200,9 +206,6 @@ def extract_questions(sentences: List[str]) -> List[str]:
all_questions = [] # Initialise an empty list to hold all extracted questions
# Iterate over each sentence in the input list
for sentence in sentences:
# Skip sentences that likely represent URLs
if "/" in sentence and sentence.count("/") > 2:
continue
# Find all matches in the sentence
potential_questions = re.findall(
question_pattern, sentence, flags=re.IGNORECASE
Expand Down