@inproceedings{liu-etal-2021-texsmart,
    title = "{T}ex{S}mart: A System for Enhanced Natural Language Understanding",
    author = "Liu, Lemao  and
      Zhang, Haisong  and
      Jiang, Haiyun  and
      Li, Yangming  and
      Zhao, Enbo  and
      Xu, Kun  and
      Song, Linfeng  and
      Zheng, Suncong  and
      Zhou, Botong  and
      Zhu, Dick  and
      Feng, Xiao  and
      Chen, Tao  and
      Yang, Tao  and
      Yu, Dong  and
      Zhang, Feng  and
      Kang, ZhanHui  and
      Shi, Shuming",
    editor = "Ji, Heng  and
      Park, Jong C.  and
      Xia, Rui",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-demo.1",
    doi = "10.18653/v1/2021.acl-demo.1",
    pages = "1--10",
    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.",
}