Domain Representative Keywords Selection: A Probabilistic Approach

• URL: http://arxiv.org/abs/2203.10365v1
• Date: Sat, 19 Mar 2022 18:04:12 GMT
• Title: Domain Representative Keywords Selection: A Probabilistic Approach
• Authors: Pritom Saha Akash, Jie Huang, Kevin Chen-Chuan Chang, Yunyao Li, Lucian Popa, ChengXiang Zhai
• Abstract summary: We propose a probabilistic approach to select a subset of a textittarget domain representative keywords from a candidate set, contrasting with a context domain. We introduce an textitoptimization algorithm for selecting the subset from the generated candidate distribution. Experiments on multiple domains demonstrate the superiority of our approach over other baselines for the tasks of keyword summary generation and trending keywords selection.
• Score: 39.24258854355122
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a probabilistic approach to select a subset of a \textit{target domain representative keywords} from a candidate set, contrasting with a context domain. Such a task is crucial for many downstream tasks in natural language processing. To contrast the target domain and the context domain, we adapt the \textit{two-component mixture model} concept to generate a distribution of candidate keywords. It provides more importance to the \textit{distinctive} keywords of the target domain than common keywords contrasting with the context domain. To support the \textit{representativeness} of the selected keywords towards the target domain, we introduce an \textit{optimization algorithm} for selecting the subset from the generated candidate distribution. We have shown that the optimization algorithm can be efficiently implemented with a near-optimal approximation guarantee. Finally, extensive experiments on multiple domains demonstrate the superiority of our approach over other baselines for the tasks of keyword summary generation and trending keywords selection.

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