@inproceedings{029cd2fd78104b8a8fa034a367c63a18,
title = "The singular value decomposition-based anchor word selection method for separable nonnegative matrix factorization",
abstract = "One of the recent methods for the topic modeling is separable nonnegative matrix factorization (SNMF). In general, SNMF consists of three main steps, which are, generating a word co-occurrence matrix, selecting anchor words, and recovering a topic matrix. The anchor words strongly influence the interpretability of extracted topics. In this paper, we propose a new method for selecting the anchor words by using singular value decomposition (SVD). We assume that the most dominant words in each latent semantics created by SVD are the potential candidates for the anchor words. Our simulations show that the SVD-based anchor word selection method can reach better interpretability scores of extracted topics than the common convex hull-based method on two of three datasets.",
keywords = "anchor words, separable nonnegative matrix factorization, singular value decomposition, topic modeling",
author = "Delano Novrilianto and Hendri Murfi and Arie Wibowo",
note = "Funding Information: This work was supported by Universitas Indonesia under PITTA 2017 grant. Any opinions, findings, and conclusions or recommendations are the authors' and do not necessarily reflect those of the sponsor. Publisher Copyright: {\textcopyright} 2017 IEEE.; 21st International Conference on Asian Language Processing, IALP 2017 ; Conference date: 05-12-2017 Through 07-12-2017",
year = "2018",
month = feb,
day = "21",
doi = "10.1109/IALP.2017.8300600",
language = "English",
series = "Proceedings of the 2017 International Conference on Asian Language Processing, IALP 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "289--292",
editor = "Rong Tong and Minghui Dong and Yanfeng Lu and Yue Zhang",
booktitle = "Proceedings of the 2017 International Conference on Asian Language Processing, IALP 2017",
address = "United States",
}