![]() Finally, we further combine the modeling of global and local context for ranking. Then, we proposed a new centrality computation method to capture local salient information based on the graph structure. In terms of the local view, we first build a graph structure based on the document where phrases are regarded as vertices and the edges are similarities between vertices. From a global view, we calculate the similarity between a certain phrase and the whole document in the vector space as transitional embedding based models do. ![]() In this paper, we propose a novel method for UKE, where local and global contexts are jointly modeled. Generally, these methods simply calculate similarities between phrase embeddings and document embedding, which is insufficient to capture different context for a more effective UKE model. Publisher = "Association for Computational Linguistics",Ībstract = "Embedding based methods are widely used for unsupervised keyphrase extraction (UKE) tasks. Cite (Informal): Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context (Liang et al., EMNLP 2021) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: Video: Code = "Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context",īooktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",Īddress = "Online and Punta Cana, Dominican Republic", Association for Computational Linguistics. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 155–164, Online and Punta Cana, Dominican Republic. Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context. Anthology ID: 2021.emnlp-main.14 Volume: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing Month: November Year: 2021 Address: Online and Punta Cana, Dominican Republic Venue: EMNLP SIG: Publisher: Association for Computational Linguistics Note: Pages: 155–164 Language: URL: DOI: 10.18653/v1/2021.emnlp-main.14 Bibkey: liang-etal-2021-unsupervised Cite (ACL): Xinnian Liang, Shuangzhi Wu, Mu Li, and Zhoujun Li. Additional ablation study shows that both the local and global information is crucial for unsupervised keyphrase extraction tasks. The results show that our model outperforms most models while generalizing better on input documents with different domains and length. We evaluate our models on three public benchmarks (Inspec, DUC 2001, SemEval 2010) and compare with existing state-of-the-art models. ![]() ![]() Abstract Embedding based methods are widely used for unsupervised keyphrase extraction (UKE) tasks.
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