TexSmart: A Text Understanding System for Fine-Grained NER and Enhanced Semantic Analysis
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Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Dec 2020.
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Citing papers per year: 2021: 4; 2022: 5.
9 papers.
- 2022Generalizing to the Future: Mitigating Entity Bias in Fake News Detectioncited by 38
- 2021Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learningcited by 25
- 2021Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correctioncited by 19
- 2021TranSmart: A Practical Interactive Machine Translation Systemcited by 17
- 2021Rethinking Negative Sampling for Handling Missing Entity Annotationscited by 12
- 2022Exploring and Adapting Chinese GPT to Pinyin Input Methodcited by 8
- 2022Effidit: Your AI Writing Assistantcited by 8
- 2022"Is Whole Word Masking Always Better for Chinese BERT?": Probing on Chinese Grammatical Error Correctioncited by 4
- 2022MarkBERT: Marking Word Boundaries Improves Chinese BERTcited by 2
- 2022Effidit: Your AI Writing Assistant3 Aug 2022
- 2022Generalizing to the Future: Mitigating Entity Bias in Fake News Detection20 Apr 2022
- 2022MarkBERT: Marking Word Boundaries Improves Chinese BERT12 Mar 2022
- 2022"Is Whole Word Masking Always Better for Chinese BERT?": Probing on Chinese Grammatical Error Correction1 Mar 2022
- 2022Exploring and Adapting Chinese GPT to Pinyin Input Method1 Mar 2022
- 2021Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning23 Sep 2021
- 2021Rethinking Negative Sampling for Handling Missing Entity Annotations26 Aug 2021
- 2021Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correction3 Jun 2021
- 2021TranSmart: A Practical Interactive Machine Translation System27 May 2021