LTP: A Chinese Language Technology Platform

By Wanxiang Che, Zhenghua Li, Ting-Wei Liu. 2010

499 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yiming Cui
2. Ting-Wei Liu
3. Hongwei Wang
4. Xuanwei Zhang
5. Ziqing Yang
6. Bing Qin
7. Wanxiang Che
8. Xing Xie

Citations in the corpus

• SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction (2017, background): “e tweet are scattered around. Specifically, for celebrity termc, the coordinate of term ti is (ri,θi), where ri is the inverse of distance between c and ti in syntax dependence graph generated by LTP [4], and θi = SO(ti)·π. The overall sentiment towards the celebrity c is, therefore, approximated as”
• A Span-Extraction Dataset for Chinese Machine Reading Comprehension (2019, methodology): “Before annotating, the document is divided into several passages, and each passage is limited to have no more than 500 Chinese words, where the word is counted by us-ing LTP (Che et al., 2010).”
• Consensus Attention-based Neural Networks for Chinese Reading Comprehension (2016, methodology): “The part-of-speech and sentence segmentation is identified using LTP Toolkit (Che et al., 2010).”
• CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model (2020, methodology): “• To generate pre-trained format data, we use pyltp (Che et al., 2010) to separate text content into sentences, one complete sentence per line.”
• Learning Semantic Hierarchies via Word Embeddings (2014, methodology): “The Chinese segmentation is provided by the open-source Chinese language processing platform LTP5 (Che et al., 2010).”
• Dataset for the First Evaluation on Chinese Machine Reading Comprehension (2018, methodology): “• Pre-processing : For each sentence in the document, we do word segmentation, POS tagging and dependency parsing using LTP toolkit (Che et al., 2010).”
• Personalized Reason Generation for Explainable Song Recommendation (2019, methodology): “We utilize the public API of Language Technology Platform(3) (LTP) for POS parsing [61, 62].”
• Revisiting Pre-Trained Models for Chinese Natural Language Processing (2020, methodology): “In order to identify the boundary of Chinese words, we use LTP (Che et al., 2010) for Chinese word segmentation.”
• A review of natural language processing techniques for opinion mining systems (2017, methodology): “LTP provides SVM and maximum entropy model based approaches for POS tagging and named entity recognition, which is different from FudanNLP and NiuParser.”
• Topic-to-Essay Generation with Neural Networks (2018, methodology): “Moreover, we employ Language Technology Platform [Che et al. , 2010] to do Chinese word segmentation.”
• A Sentence Cloze Dataset for Chinese Machine Reading Comprehension (2020, background): “le reading comprehension task, we select several sentences in the passages and replace with special marks (for example, [BLANK]), forming an incomplete passage. The sentences are identified using LTP (Che et al., 2010), and we further split the sentence with comma and period mark, as some of the sent”
• Pre-Training with Whole Word Masking for Chinese BERT (2019, methodology): “In order to identify the boundary of Chinese words, we use LTP 6 (Che et al., 2010) for Chinese Word Segmentation (CWS).”