Paper Info
Title
Texsmart: A System For Enhanced Natural Language Understanding
Abstract
This paper introduces TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities. Compared to most previous publicly available text understanding systems and tools, TexSmart holds some unique features. First, the NER function of TexSmart supports over 1,000 entity types, while most other public tools typically support several to (at most) dozens of entity types. Second, TexSmart introduces new semantic analysis functions like semantic expansion and deep semantic representation, that are absent in most previous systems. Third, a spectrum of algorithms (from very fast algorithms to those that are relatively slow but more accurate) are implemented for one function in TexSmart, to fulfill the requirements of different academic and industrial applications. The adoption of unsupervised or weakly-supervised algorithms is especially emphasized, with the goal of easily updating our models to include fresh data with less human annotation efforts.(1)
Year
DOI
Venue
2021
10.18653/v1/2021.acl-demo.1
ACL-IJCNLP 2021: THE JOINT CONFERENCE OF THE 59TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 11TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING: PROCEEDINGS OF THE SYSTEM DEMONSTRATIONS
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
17
Name
Order
Citations
PageRank
Lemao Liu18718.74
Haisong Zhang2158.00
Haiyun Jiang301.01
Yangming Li400.68
Enbo Zhao500.34
Kun Xu600.34
Linfeng Song700.34
Suncong Zheng8847.61
Botong Zhou900.34
Jianchen Zhu1000.34
Xiao Feng1100.34
Tao Chen1200.34
Tao Yang1300.34
Dong Yu146264475.73
Feng Zhang1522825.71
Zhanhui Kang1600.34
Shuming Shi1762058.27