# Domain Representative Keywords Selection: A Probabilistic Approach

arXiv: https://arxiv.org/abs/2203.10365 · HTML: https://mlsanity.com/paper/2203.10365/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 19 Mar 2022.

## Builds on (most cited first, 7 of 7)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Deep Keyphrase Generation](https://mlsanity.com/paper/1704.06879/) (2017)
- [Automated Phrase Mining from Massive Text Corpora](https://mlsanity.com/paper/1702.04457/) (2017)
- [Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision](https://mlsanity.com/paper/1901.01151/) (2019)
- [Simple Unsupervised Keyphrase Extraction using Sentence Embeddings](https://mlsanity.com/paper/1801.04470/) (2018)
- [DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging](https://mlsanity.com/paper/1707.04596/) (2017)
- [Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach](https://mlsanity.com/paper/2105.13255/) (2021)

## 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.