Instead of mining coherent topics from a given text corpus in a completely unsupervised manner, seed-guided topic discovery methods leverage user-provided seed words to extract distinctive and coherent topics so that the mined topics can better cater to the user's interest. To model the semantic correlation between words and seeds for discovering topic-indicative terms, existing seed-guided approaches utilize different types of context signals, such as document-level word co-occurrences, sliding window-based local contexts, and generic linguistic knowledge brought by pre-trained language models. In this work, we analyze and show empirically that each type of context information has its value and limitation in modeling word semantics under seed guidance, but combining three types of contexts (i.e., word embeddings learned from local contexts, pre-trained language model representations obtained from general-domain training, and topic-indicative sentences retrieved based on seed information) allows them to complement each other for discovering quality topics. We propose an iterative framework, SeedTopicMine, which jointly learns from the three types of contexts and gradually fuses their context signals via an ensemble ranking process. Under various sets of seeds and on multiple datasets, SeedTopicMine consistently yields more coherent and accurate topics than existing seed-guided topic discovery approaches.

本文提出一种基于三种不同上下文信号的种子引导主题发现方法，该方法结合了来自本地上下文的词嵌入，来自通用域训练的预先训练语言模型表示以及基于种子信息检索的主题指示句子，证明了三种类型的语境信息的价值和局限性在种子引导下的建模词语语义，并提出了一种迭代框架SeedTopicMine，该框架通过集成排名过程逐步融合了它们的上下文信号，相较于现有的种子引导主题发现方法，提供更一致和准确的主题。

通过整合多种类型的上下文，实现有效的种子引导主题发现