Descriptive document clustering via discriminant learning in a co-embedded space of multilevel similarities

Mu, Tingting, Goulermas, John Y., Korkontzelos, Ioannis and Ananiadou, Sophia (2016) Descriptive document clustering via discriminant learning in a co-embedded space of multilevel similarities. Journal of the Association for Information Science and Technology, 67 (1). pp. 106-133. ISSN 2330-1635 DOI

CEDL manuscript.pdf
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (5MB) | Preview


Descriptive document clustering aims at discovering clusters of semantically interrelated documents together with meaningful labels to summarize the content of each document cluster. In this work, we propose a novel descriptive clustering framework, referred to as CEDL. It relies on the formulation and generation of 2 types of heterogeneous objects, which correspond to documents and candidate phrases, using multilevel similarity information. CEDL is composed of 5 main processing stages. First, it simultaneously maps the documents and candidate phrases into a common co-embedded space that preserves higher-order, neighbor-based proximities between the combined sets of documents and phrases. Then, it discovers an approximate cluster structure of documents in the common space. The third stage extracts promising topic phrases by constructing a discriminant model where documents along with their cluster memberships are used as training instances. Subsequently, the final cluster labels are selected from the topic phrases using a ranking scheme using multiple scores based on the extracted co-embedding information and the discriminant output. The final stage polishes the initial clusters to reduce noise and accommodate the multitopic nature of documents. The effectiveness and competitiveness of CEDL is demonstrated qualitatively and quantitatively with experiments using document databases from different application fields.

Item Type: Article
Uncontrolled Keywords: machine learning, unsupervised clustering, natural language processing, text mining, information retrieval
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computing and Information Systems
Date Deposited: 01 Apr 2016 11:57

Archive staff only

Item control page Item control page