Probabilistic Dynamic Non-negative Group Factor Model for Multi-source Text Mining

Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

2 Lataukset (Pure)

Abstrakti

Nonnegative matrix factorization (NMF) is a popular approach to model data, however, most models are unable to flexibly take into account multiple matrices across sources and time or apply only to integer-valued data. We introduce a probabilistic, Gaussian Process-based, more inclusive NMF-based model which jointly analyzes nonnegative data such as text data word content from multiple sources in a temporal dynamic manner. The model collectively models observed matrix data, source-wise latent variables, and their dependencies and temporal evolution with a full-fledged hierarchical approach including flexible nonparametric temporal dynamics. Experiments on simulated data and real data show the model out-performs, comparable models. A case study on social media and news demonstrates the model discovers semantically meaningful topical factors and their evolution.
AlkuperäiskieliEnglanti
OtsikkoCIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management
KustantajaACM
Sivut1035-1043
Sivumäärä9
ISBN (elektroninen)978-1-4503-6859-9
DOI - pysyväislinkit
TilaJulkaistu - lokak. 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaACM International Conference on Information & Knowledge Management -
Kesto: 19 lokak. 202023 lokak. 2020

Conference

ConferenceACM International Conference on Information & Knowledge Management
LyhennettäCIKM
Ajanjakso19/10/2023/10/20

Julkaisufoorumi-taso

  • Jufo-taso 2

Sormenjälki

Sukella tutkimusaiheisiin 'Probabilistic Dynamic Non-negative Group Factor Model for Multi-source Text Mining'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä