Bringing structure to text

Jiawei Han, Chi Wang, Ahmed El-Kishky
2014 Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '14  
Mining phrases, entity concepts, topics, and hierarchies from massive text corpus is an essential problem in the age of big data. Text data in electronic forms are ubiquitous, ranging from scientific articles to social networks, enterprise logs, news articles, social media and general web pages. It is highly desirable but challenging to bring structure to unstructured text data, uncover underlying hierarchies, relationships, patterns and trends, and gain knowledge from such data. In this
more » ... l, we provide a comprehensive survey on the state-of-the art of data-driven methods that automatically mine phrases, extract and infer latent structures from text corpus, and construct multi-granularity topical groupings and hierarchies of the underlying themes. We study their principles, methodologies, algorithms and applications using several real datasets including research papers and news articles and demonstrate how these methods work and how the uncovered latent entity structures may help text understanding, knowledge discovery and management.
doi:10.1145/2623330.2630804 dblp:conf/kdd/HanWE14 fatcat:wcq4brxwnvf4nkpiv5txqkdwuy