| import pandas as pd |
| import stanza |
|
|
| nlp = stanza.Pipeline(lang='en', processors='tokenize,pos,lemma,depparse') |
| articles = ['a', 'an', 'the'] |
|
|
| |
| INPUT_FILE = 'compactIE_predictions.txt' |
|
|
|
|
| def verb_count(part, sent): |
| s_doc = nlp(sent) |
| text2token = {} |
| for i in range(len(s_doc.sentences)): |
| tokens = [word.to_dict() for word in s_doc.sentences[i].words] |
| for t in tokens: |
| text2token[t["text"]] = t |
| doc = nlp(part) |
| doc = doc.sentences[0] |
| tokens = [word.to_dict() for word in doc.words] |
| verbs = 0 |
| for token in tokens: |
| if (token['upos'] == 'VERB' and (token['deprel'] not in ['xcomp', 'amod', 'case', 'obl'])) or (token['upos'] == "AUX" and token['deprel'] == 'cop'): |
| try: |
| if text2token[token["text"]]["deprel"] == token['deprel']: |
| |
| verbs += 1 |
| except: |
| continue |
| return verbs |
|
|
|
|
| def clausal_constituents(extraction): |
| clausal_consts = 0 |
| if extraction["predicate"].strip() != "": |
| pred_count = verb_count(extraction["predicate"], extraction["sentence"]) |
| if pred_count > 1: |
| clausal_consts += pred_count - 1 |
|
|
| if extraction["subject"].strip() != "": |
| clausal_consts += verb_count(extraction["subject"], extraction["sentence"]) |
|
|
| if extraction["object"].strip() != "": |
| clausal_consts += verb_count(extraction["object"], extraction["sentence"]) |
| |
| |
| return clausal_consts |
|
|
|
|
| if __name__ == "__main__": |
| extractions_df = pd.DataFrame(columns=["sentence", "subject", "predicate", "object"]) |
|
|
| with open(INPUT_FILE, 'r') as f: |
| lines = f.readlines() |
|
|
| sentences = set() |
| for line in lines: |
| sentence, ext, score = line.split('\t') |
| sentences.add(sentence) |
| try: |
| arg1 = ext[ext.index('<arg1>') + 6:ext.index('</arg1>')] |
| except: |
| arg1 = "" |
| try: |
| rel = ext[ext.index('<rel>') + 5:ext.index('</rel>')] |
| except: |
| rel = "" |
| try: |
| arg2 = ext[ext.index('<arg2>') + 6:ext.index('</arg2>')] |
| except: |
| arg2 = "" |
| row = pd.DataFrame( |
| {"sentence": [sentence], |
| "subject": [arg1], |
| "predicate": [rel], |
| "object": [arg2]} |
| ) |
| extractions_df = pd.concat([extractions_df, row]) |
|
|
| |
| grouped_df = extractions_df.groupby("sentence") |
| total_number_of_arguments = 0 |
| number_of_unique_arguments = 0 |
| num_of_sentences = len(grouped_df.groups.keys()) |
| for sent in grouped_df.groups.keys(): |
| per_sentence_argument_set = set() |
| sen_group = grouped_df.get_group(sent).reset_index(drop=True) |
| extractions_list = list(sen_group.T.to_dict().values()) |
| for extr in extractions_list: |
| if extr["subject"] not in ['', 'nan']: |
| total_number_of_arguments += 1 |
| per_sentence_argument_set.add(extr["subject"]) |
| if extr["object"] not in ['', 'nan']: |
| total_number_of_arguments += 1 |
| per_sentence_argument_set.add(extr["object"]) |
| number_of_unique_arguments += len(per_sentence_argument_set) |
|
|
| print("average # repetitions per argument: {}".format(total_number_of_arguments/number_of_unique_arguments)) |
| print("average # extractions per sentence: {}".format(extractions_df.shape[0]/len(sentences))) |
| avg_arguments_size = 0.0 |
| for sent in sentences: |
| extractions_per_sent = extractions_df[extractions_df["sentence"] == sent] |
|
|
| sent_extractions = extractions_per_sent.shape[0] |
| extractions_per_sent["avg_arg_length"] = extractions_per_sent.apply(lambda r: (len(str(r["subject"]).split(' ')) + len(str(r["predicate"]).split(' ')) + len(str(r["object"]).split(' ')))/3, axis=1) |
| avg_arguments_size += sum(extractions_per_sent["avg_arg_length"].values.tolist()) / extractions_per_sent.shape[0] |
|
|
| print("average length of constituents (per sentence, per extraction): ", avg_arguments_size/len(sentences)) |
| extractions_df["clause_counts"] = extractions_df.apply(lambda r: clausal_constituents(r), axis=1) |
| avg_clause_count = sum(extractions_df["clause_counts"].values.tolist()) / len(sentences) |
| print("number of verbal clauses within arguments: ", avg_clause_count) |
|
|
