Word2Cluster: A new multi-label text clustering algorithm with an adaptive clusters number
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings, 2019, 00, pp. 1-6
- Issue Date:
- 2019-12-01
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Word2Cluster.pdf | Published version | 403.18 kB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Text clustering has been widely used in many Natural Language Processing (NLP) applications such as text summarization and news recommendation. However, most of the current algorithms need to predefine a clustering number, which is difficult to obtain. Moreover, the mutli-label clustering is useful in multiple clustering tasks in many applications, but related works are rarely available. Although several studies have attempted to solve above two problems, there is a need for methods that can solve the two issues simultaneously. Therefore, we propose a new text clustering algorithm called Word2Cluster. Word2Cluster can automatically generate an adaptive number of clusters and support multi-label clustering. To test the performance of Wrod2Cluster, we build a Chinese text dataset, Hotline, according to real world applications. To evaluate the clustering results better, we propose an improved evaluation method based on basic accuracy, precision and recall for multi-label text clustering. Experimental results on a Chinese text dataset (Hotline) and a public English text dataset (Reuters) demonstrate that our algorithm can achieve better F1-measure and runs faster than the state-of- the-art baselines.
Please use this identifier to cite or link to this item: