利用句子嵌入预测奥斯卡提名剧本
Predicting Oscar-Nominated Screenplays with Sentence Embeddings
AI总结：
本文构建 Movie-O-Label 数据集，利用 E5 句子嵌入和逻辑回归模型，通过融合剧本、摘要和标题特征预测奥斯卡提名，最佳模型宏 F1 达 0.66，验证了简单嵌入模型的有效性。
摘要：
奥斯卡提名是电影行业中的一个重要因素，因为它们能够提升电影的知名度和商业成功。本研究探讨了是否可以利用现代语言模型来预测剧本获得奥斯卡提名。由于缺乏合适的数据集，我们构建了一个名为 Movie-O-Label 的新数据集，将 MovieSum 电影剧本集合与经过整理的奥斯卡记录相结合。每个剧本由其标题、维基百科摘要和完整剧本表示。长剧本被分割成重叠的文本块，并使用 E5 句子嵌入模型进行编码。然后，使用逻辑回归模型对剧本嵌入进行分类。当结合与剧本相关的三种特征输入（剧本、摘要和标题）时，取得了最佳结果。表现最好的模型达到了 0.66 的宏 F1 分数、0.445 的精确率-召回率 AP（基线为 0.19）以及 0.79 的 ROC-AUC。结果表明，即使是基于现代文本嵌入的简单模型也展现出良好的预测性能，并可能成为未来研究的起点。
英文摘要：
Oscar nominations are an important factor in the movie industry because they can boost both the visibility and the commercial success. This work explores whether it is possible to predict Oscar nominations for screenplays using modern language models. Since no suitable dataset was available, a new one called Movie-O-Label was created by combining the MovieSum collection of movie scripts with curated Oscar records. Each screenplay was represented by its title, Wikipedia summary, and full script. Long scripts were split into overlapping text chunks and encoded with the E5 sentence em bedding model. Then, the screenplay embed dings were classified using a logistic regression model. The best results were achieved when three feature inputs related to screenplays (script, summary, and title) were combined. The best-performing model reached a macro F1 score of 0.66, a precision recall AP of 0.445 with baseline 0.19 and a ROC-AUC of 0.79. The results suggest that even simple models based on modern text embeddings demonstrate good prediction performance and might be a starting point for future research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
