Domain Representative Keywords Selection: A Probabilistic Approach
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Abstract (arXiv)
We propose a probabilistic approach to select a subset of a textittarget 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 textittwo-component mixture model concept to generate a distribution of candidate keywords. It provides more importance to the textitdistinctive keywords of the target domain than common keywords contrasting with the context domain. To support the textitrepresentativeness of the selected keywords towards the target domain, we introduce an textitoptimization 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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References · 7
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
- 2017Deep Keyphrase Generationcited by 102
- 2017Automated Phrase Mining from Massive Text Corporacited by 70
- 2019Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Visioncited by 64
- 2018Simple Unsupervised Keyphrase Extraction using Sentence Embeddingscited by 62
- 2017DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Taggingcited by 5
- 2021Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approachcited by 2
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