论文标题
种子引导的主题发现与烟代外种子
Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds
论文作者
论文摘要
从文本语料库中发现潜在主题已有数十年了。许多现有的主题模型采用了完全无监督的设置，由于无法利用用户指导，他们发现的主题可能无法满足用户的特殊兴趣。尽管存在种子引导的主题发现方法，该方法利用用户提供的种子来发现主题代表性的术语，但它们不太关心两个因素：（1）存在量不足的种子，以及（2）预先训练的语言模型（PLMS）的力量。在本文中，我们概括了种子引导的主题发现的任务，以允许使用量的种子。我们提出了一个名为Seetopic的新型框架，其中PLM的一般知识和从输入语料库中学到的局部语义可以互相受益。来自不同领域的三个真实数据集的实验证明了对主题连贯性，准确性和多样性的有效性。
Discovering latent topics from text corpora has been studied for decades. Many existing topic models adopt a fully unsupervised setting, and their discovered topics may not cater to users' particular interests due to their inability of leveraging user guidance. Although there exist seed-guided topic discovery approaches that leverage user-provided seeds to discover topic-representative terms, they are less concerned with two factors: (1) the existence of out-of-vocabulary seeds and (2) the power of pre-trained language models (PLMs). In this paper, we generalize the task of seed-guided topic discovery to allow out-of-vocabulary seeds. We propose a novel framework, named SeeTopic, wherein the general knowledge of PLMs and the local semantics learned from the input corpus can mutually benefit each other. Experiments on three real datasets from different domains demonstrate the effectiveness of SeeTopic in terms of topic coherence, accuracy, and diversity.
