| Download | - View final version: Real-time change point detection using on-line topic models (PDF, 841 KiB)
|
|---|
| Author | Search for: Wang, Yunli1; Search for: Goutte, Cyril1 |
|---|
| Affiliation | - National Research Council of Canada. Digital Technologies
|
|---|
| Format | Text, Article |
|---|
| Conference | The 27th International Conference on Computational Linguistics, August 20-26, 2018, Santa Fe, New Mexico, United States |
|---|
| Abstract | Detecting changes within an unfolding event in real time from news articles or social media enables to react promptly to serious issues in public safety, public health or natural disasters. In this study, we use on-line Latent Dirichlet Allocation (LDA) to model shifts in topics, and apply on-line change point detection (CPD) algorithms to detect when significant changes happen. We describe an on-line Bayesian change point detection algorithm that we use to detect topic changes from on-line LDA output. Extensive experiments on social media data and news articles show the benefits of on-line LDA versus standard LDA, and of on-line change point detection compared to off-line algorithms. This yields F-scores up to 56% on the detection of significant real-life changes from these document streams. |
|---|
| Publication date | 2018-08 |
|---|
| Publisher | Association for Computational Linguistics |
|---|
| In | |
|---|
| Language | English |
|---|
| Peer reviewed | Yes |
|---|
| Export citation | Export as RIS |
|---|
| Report a correction | Report a correction (opens in a new tab) |
|---|
| Record identifier | ae468b4e-c80f-4e4a-8546-8c92f9584cae |
|---|
| Record created | 2019-05-29 |
|---|
| Record modified | 2021-09-20 |
|---|
