TY - GEN
T1 - Automatic entity recognition and typing from massive text corpora
T2 - 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015
AU - Ren, Xiang
AU - El-Kishky, Ahmed
AU - Wang, Chi
AU - Han, Jiawei
PY - 2015/8/10
Y1 - 2015/8/10
N2 - In today's computerized and information-based society, we are soaked with vast amounts of text data, ranging from news articles, scientific publications, product reviews, to a wide range of textual information from social media. To unlock the value of these unstructured text data from various domains, it is of great importance to gain an understanding of entities and their relationships. In this tutorial, we introduce data-driven methods to recognize typed entities of interest in massive, domain-specific text corpora. These methods can automatically identify token spans as entity mentions in documents and label their types (e.g., people, product, food) in a scalable way. We demonstrate on real datasets including news articles and tweets how these typed entities aid in knowledge discovery and management.
AB - In today's computerized and information-based society, we are soaked with vast amounts of text data, ranging from news articles, scientific publications, product reviews, to a wide range of textual information from social media. To unlock the value of these unstructured text data from various domains, it is of great importance to gain an understanding of entities and their relationships. In this tutorial, we introduce data-driven methods to recognize typed entities of interest in massive, domain-specific text corpora. These methods can automatically identify token spans as entity mentions in documents and label their types (e.g., people, product, food) in a scalable way. We demonstrate on real datasets including news articles and tweets how these typed entities aid in knowledge discovery and management.
UR - http://www.scopus.com/inward/record.url?scp=84954102418&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84954102418&partnerID=8YFLogxK
U2 - 10.1145/2783258.2789988
DO - 10.1145/2783258.2789988
M3 - Conference contribution
AN - SCOPUS:84954102418
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 2319
EP - 2320
BT - KDD 2015 - Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
Y2 - 10 August 2015 through 13 August 2015
ER -