地球资源数据云——数据资源详情

NLP SubjQA：问答数据集

该数据集《NLP SubjQA: Question Answering Dataset》主要用于多分类任务，数据形态以文本为主，应用场景偏向文本内容分析。 题目说明：Test your NLP skills to build a QA system by subjectivity 任务类型：文本多分类。 建议流程：先做文本清洗与分词，再比较 TF - IDF+线性模型 与 预训练语言模型。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：未检测到标准 CSV，可优先查看目录中的索引或说明文件。 Context Subjectivity is the expression of internal opinions or beliefs which cannot be objectively observed or verified, and has been shown to be important for sentiment analysis and wordsense disambiguation. Furthermore, subjectivity is an important aspect of user - generated data. In spite of this, subjectivity has not been investigated in contexts where such data is widespread, such as in question answering (QA). This new dataset allows us to investigate this relationship. Subjectivity is an important feature in the case of QA, albeit with more intricate interactions between subjectivity and QA performance than found in previous works on sentiment analysis. For instance, a subjective question may or may not be associated with a subjective answer. gif . NLP — Building a Question Answering model