引言
RAMS数据集（RAMS：Richly Annotated Multilingual Schema-guided Event Structure）由约翰斯·霍普金斯大学于2020年发布，是一个以新闻为基础的事件抽取数据集。它标注了9,124个事件，涵盖了139种不同的事件类型和65种元素角色类型。事件类型涉及多个领域，如：
- 生命事件（life）
- 冲突事件（conflict）
- 灾难事件（disaster）
- 司法事件（justice）
- 联络事件（contact）
- 政府事件（government）
而元素角色类型包括如：
- 地点（place）
- 参与者（participant）
- 目的地（destination）
- 起源（origin）
- 受害者（victim）
- 被告人（defendant）
这个数据集非常适合用于事件抽取、自然语言处理任务，特别是对事件结构、事件角色的识别和分类。
一、特点（features）
- 事件类型多样化：涵盖多个领域，增强了事件抽取任务的广泛性和复杂性。
- 角色标注详细：为每个事件详细标注了不同的角色，为构建事件图、进行因果推理等任务提供了丰富的上下文信息。
- 结构化标注：不仅仅提供文本，还为每个事件及其参与者标注了详细的语义信息，使其适用于高层次的文本分析。
二、下载（download）
- 可以通过访问官方下载网站进行最新和历史数据集的下载。
- 也可以通过访问我的主页提供的数据集来进行下载。
三、数据集（database）
3.1 数据
数据被分成 train/dev/test 三个文件，
每个数据文件的每一行包含一个 json 字符串，
每个 json 包含：
ent_spans：开始和结束（包含）索引以及事件/参数/角色字符串。evt_triggers：开始和结束（包括）索引以及事件类型字符串。sentences：文档文本gold_evt_links：遵循上述格式的三元组（事件、论点、角色）source_url：文本来源split：它属于哪个数据分割doc_key：它对应于哪个单独的文件（
所有其他字段都是多余的，以允许 RAMS 的未来迭代。
格式化之后的一条数据（train.jsonlines的第1行）如下展示：
{"rel_triggers": [],"gold_rel_links": [],"doc_key": "nw_RC000462ebb18ca0b29222d5e557fa31072af8337e3a0910dca8b5b62f","ent_spans": [[42,43,[["evt090arg02victim",1.0]]],[85,88,[["evt090arg01killer",1.0]]],[26,26,[["evt090arg04place",1.0]]]],"language_id": "eng","source_url": "https://www.washingtonpost.com/news/powerpost/paloma/daily-202/2016/06/17/daily-202-more-republicans-ditch-trump-conclude-he-cannot-win/5763a1e0981b92a22d0f8a36/","evt_triggers": [[69,69,[["life.die.deathcausedbyviolentevents",1.0]]]],"split": "train","sentences": [["Transportation","officials","are","urging","carpool","and","teleworking","as","options","to","combat","an","expected","flood","of","drivers","on","the","road","."],["(","Paul","Duggan",")"],["--","A","Baltimore","prosecutor","accused","a","police","detective","of","\u201c","sabotaging","\u201d","investigations","related","to","the","death","of","Freddie","Gray",",","accusing","him","of","fabricating","notes","to","suggest","that","the","state","\u2019s","medical","examiner","believed","the","manner","of","death","was","an","accident","rather","than","a","homicide","."],["The","heated","exchange","came","in","the","chaotic","sixth","day","of","the","trial","of","Baltimore","Officer","Caesar","Goodson","Jr.",",","who","drove","the","police","van","in","which","Gray","suffered","a","fatal","spine","injury","in","2015","."],["(","Derek","Hawkins","and","Lynh","Bui",")"]],"gold_evt_links": [[[69,69],[85,88],"evt090arg01killer"],[[69,69],[42,43],"evt090arg02victim"],[[69,69],[26,26],"evt090arg04place"]]
}1. sentences：
- 文档内容被分为多个句子：
- 句子1："Transportation officials are urging carpool and teleworking as options to combat an expected flood of drivers on the road."
- 句子2："(Paul Duggan)"
- 句子3："A Baltimore prosecutor accused a police detective of ‘sabotaging’ investigations related to the death of Freddie Gray."
- 句子4："The heated exchange came in the chaotic sixth day of the trial of Baltimore Officer Caesar Goodson Jr."
2. evt_triggers（事件触发器）：
[69, 69]对应的词是句子3中的 "homicide"，标注事件类型为 "life.die.deathcausedbyviolentevents"（与暴力事件导致的死亡相关）。
3. ent_spans（实体标注，开始和结束索引，以及事件角色）：
[42, 43]对应的词是句子3中的 "Freddie Gray"，角色为 "victim"（受害者）。[85, 88]对应的词是句子4中的 "Caesar Goodson Jr."，角色为 "killer"（凶手）。[26, 26]对应的词是句子3中的 "Baltimore"，角色为 "place"（地点）。
4. gold_evt_links（事件-论点-角色三元组）：
- 第一个三元组：触发词 "homicide"，论点是 "Caesar Goodson Jr."，角色是 "killer"。
- 第二个三元组：触发词 "homicide"，论点是 "Freddie Gray"，角色是 "victim"。
- 第三个三元组：触发词 "homicide"，论点是 "Baltimore"，角色是 "place"。
5. source_url：
- 文档来源是：https://www.washingtonpost.com/news/powerpost/paloma/daily-202/2016/06/17/daily-202-more-republicans-ditch-trump-conclude-he-cannot-win/5763a1e0981b92a22d0f8a36/
6. split：
- 样本属于 训练集（train）。
7. doc_key：
- 对应的文档ID为 "nw_RC000462ebb18ca0b29222d5e557fa31072af8337e3a0910dca8b5b62f"，该ID用于唯一标识文档。
四、数据处理
import jsondef load_data(file_path):data = []with open(file_path, 'r') as f:for line in f:data.append(json.loads(line))return datadef save_to_json(data, file_path):with open(file_path, 'w') as f:json.dump(data, f, indent=4)def extract_event_data(entry):sentences = [" ".join(s) for s in entry["sentences"]]text = [item for sublist in entry["sentences"] for item in sublist]# text = entry["sentences"]# text = " ".join(sentences)# 处理实体ent_spans = [(span[0], span[1], span[2][0][0]) for span in entry["ent_spans"]]# 处理事件触发词evt_triggers = [(trigger[0], trigger[1], trigger[2][0][0]) for trigger in entry["evt_triggers"]]# 处理事件-论点链接evt_links = entry["gold_evt_links"]return text, ent_spans, evt_triggers, evt_linksdef prepare_training_data(entries):dataset = []for entry in entries:text, ent_spans, evt_triggers, evt_links = extract_event_data(entry)# 生成训练样本dataset.append({'text': text,'entities': ent_spans,'triggers': evt_triggers,'links': evt_links})return datasetif __name__ == '__main__':train_data = load_data("./train.jsonlines")training_dataset = prepare_training_data(train_data)save_to_json(training_dataset, 'train.json')print(training_dataset[0])
4.1 加载并解析数据
首先，加载JSON格式的数据文件，并解析其中的字段。
import jsondef load_data(file_path):data = []with open(file_path, 'r') as f:for line in f:data.append(json.loads(line))return datatrain_data = load_data('train.json')
4.2 数据预处理
将文档中的句子、事件触发词、角色和实体进行标注与转换，以便用于事件抽取模型。我们可以提取句子、事件触发词及角色信息。
def extract_event_data(entry):sentences = [" ".join(s) for s in entry["sentences"]]text = " ".join(sentences)# 处理实体ent_spans = [(span[0], span[1], span[2][0][0]) for span in entry["ent_spans"]]# 处理事件触发词evt_triggers = [(trigger[0], trigger[1], trigger[2][0][0]) for trigger in entry["evt_triggers"]]# 处理事件-论点链接evt_links = entry["gold_evt_links"]return text, ent_spans, evt_triggers, evt_links# 示例提取
for entry in train_data:text, ent_spans, evt_triggers, evt_links = extract_event_data(entry)print(f"文本: {text}")print(f"实体: {ent_spans}")print(f"事件触发词: {evt_triggers}")print(f"事件-论点链接: {evt_links}")
4.3 生成模型输入
为了进行事件抽取，常见的输入是文本与相应的事件触发器和角色。我们可以构建一个数据集，将文本标注为序列标注任务或使用分类任务标注事件触发词和论点。
def prepare_training_data(entries):dataset = []for entry in entries:text, ent_spans, evt_triggers, evt_links = extract_event_data(entry)# 生成训练样本dataset.append({'text': text,'entities': ent_spans,'triggers': evt_triggers,'links': evt_links})return datasettraining_dataset = prepare_training_data(train_data)