| from collections import defaultdict | |
| import json | |
| import pandas as pd | |
| from sociofillmore.common.analyze_text import analyze_single_document, load_deep_frames_cache | |
| def main(): | |
| frames_of_interest = ["Killing", "Death", "Dead_or_alive", "Event", "Catastrophe", "Undergoing"] | |
| texts_df = pd.read_csv( | |
| "output/femicides/split_data/rai/split_main.texts.meta.csv") | |
| deep_frames_cache = load_deep_frames_cache() | |
| fcp_to_sentences = defaultdict(list) | |
| for i, (_, row) in enumerate(texts_df.iterrows()): | |
| if i % 100 == 0: | |
| print(i) | |
| doc_analysis = analyze_single_document(row["text_id"], row["event_id"], "lome_0shot", | |
| "femicides/rai_main", texts_df, deep_frames_cache) | |
| for i, sent_analysis in enumerate(doc_analysis): | |
| sentence = " ".join(sent_analysis["sentence"]) | |
| for fn_st in sent_analysis["fn_structures"]: | |
| frame = fn_st["frame"] | |
| tgt_idx = str(fn_st["target"]["tokens_idx"][0]) | |
| if frame in frames_of_interest: | |
| construction = sent_analysis["syntax"][tgt_idx][0]["syn_construction"] | |
| fcp_to_sentences[f"{frame}++{construction}"].append({ | |
| "event_id": row["event_id"], | |
| "frame": frame, | |
| "construction": construction, | |
| "target": fn_st["target"]["tokens_str"], | |
| "text_id": row["text_id"], | |
| "sentence_idx": i, | |
| "sentence_str": sentence, | |
| "selected_frame": frame, | |
| "selected_cx": construction | |
| }) | |
| with open("output/scoring/extracted_frames.json", "w") as f: | |
| json.dump(fcp_to_sentences, f, indent=4, sort_keys=True) | |
| if __name__ == "__main__": | |
| main() | |