论文标题
使用句子嵌入来检查事实的价值
Checking Fact Worthiness using Sentence Embeddings
论文作者
论文摘要
在文本和演讲中检查和确认事实信息对于确定事实陈述的真实性和正确性至关重要。这项工作以前是由记者和其他手动手段完成的，但这是一项耗时的任务。随着信息检索和NLP的进步，事实检查领域的研究引起了自动化的关注。 CLEF-2018和2019年组织了与事实检查和邀请参与者有关的任务。该项目着重于CLEF-2019 Task-1检查值，并使用最新句子的预先训练的嵌入，主题建模和情感得分进行实验。评估指标，例如MAP，平均互惠等级，平均R-PRECISION和平均精度@n提供了使用这些技术的结果的改进。
Checking and confirming factual information in texts and speeches is vital to determine the veracity and correctness of the factual statements. This work was previously done by journalists and other manual means but it is a time-consuming task. With the advancements in Information Retrieval and NLP, research in the area of Fact-checking is getting attention for automating it. CLEF-2018 and 2019 organised tasks related to Fact-checking and invited participants. This project focuses on CLEF-2019 Task-1 Check-Worthiness and experiments using the latest Sentence-BERT pre-trained embeddings, topic Modeling and sentiment score are performed. Evaluation metrics such as MAP, Mean Reciprocal Rank, Mean R-Precision and Mean Precision@N present the improvement in the results using the techniques.
