Back to Search Start Over

TexSmart: A Text Understanding System for Fine-Grained NER and Enhanced Semantic Analysis

Authors :
Zhang, Haisong
Liu, Lemao
Jiang, Haiyun
Li, Yangming
Zhao, Enbo
Xu, Kun
Song, Linfeng
Zheng, Suncong
Zhou, Botong
Zhu, Jianchen
Feng, Xiao
Chen, Tao
Yang, Tao
Yu, Dong
Zhang, Feng
Kang, Zhanhui
Shi, Shuming
Publication Year :
2020

Abstract

This technique report 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. The main contents of this report include major functions of TexSmart, algorithms for achieving these functions, how to use the TexSmart toolkit and Web APIs, and evaluation results of some key algorithms.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2012.15639
Document Type :
Working Paper