The Third International Chinese Language Processing Bakeoff: Word Segmentation and Named Entity Recognition

By Gina-Anne Levow. 2006

420 citations recorded by the source corpus.

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Citations in the corpus

ERNIE 2.0: A Continual Pre-training Framework for Language Understanding (2019, methodology): “• Machine Reading Comprehension (MRC): CMRC 2018 [27], DRCD [28], DuReader [29] • Named Entity Recognition (NER): MSRA-NER (SIGHAN 2006) [30] • Natural Language Inference (NLI): XNLI [31] (3)https://www.”
Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT (2019, methodology): “We use the CoNLL 2002 and 2003 NER shared tasks (Tjong Kim Sang, 2002; Tjong Kim Sang and De Meulder, 2003) (4 languages) and a Chinese NER dataset (Levow, 2006).”
Emerging Cross-lingual Structure in Pretrained Language Models (2019, methodology): “For English-Chinese, we use CoNLL 2003 English (Tjong Kim Sang and De Meulder, 2003) and a Chinese NER dataset (Levow, 2006), with realigned Chinese NER labels based on the Stanford word segmenter.”
Deep Learning for Chinese Word Segmentation and POS Tagging (2013, methodology): “The second set of experiments was run on the Chinese Treebank (CTB) data sets from Bakeoff-3 (Levow, 2006), which contains a training and a test corpus for supervised word segmentation and POS tagging tasks.”
FLAT: Chinese NER Using Flat-Lattice Transformer (2020, unknown): “For MSRA and Ontonotes these two large datasets, we select the hyper-parameters based on the development experiment of Ontonotes.”
Chinese NER Using Lattice LSTM (2018, background): “, 2011), MSRA (Levow, 2006) Weibo NER (Peng and”
Optimizing Chinese Word Segmentation for Machine Translation Performance (2008, background): “Detail of the Bakeoff data sets is in (Levow, 2006).”
Simplify the Usage of Lexicon in Chinese NER (2019, unknown): “, 2011), MSRA (Levow, 2006), Weibo NER (Peng”
ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding (2020, background): “, 2017), named entity recognition (MSRA-NER; Levow, 2006) and semantic similarity (LCQMC; Liu et al.”
Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models (2023, methodology): “0 [81, 47, 90, 46, 48], and ACE05 in both languages, comparing three metrics of precision, recall, and F1 score.”
Dice Loss for Data-imbalanced NLP Tasks (2019, methodology): “Our implementation uses the current state-of-the-art model proposed by Li et al. (2019) as the backbone, and changes the MLE loss to DSC loss.”
A Unified MRC Framework for Named Entity Recognition (2019, unknown): “MSRA (Levow, 2006) is a Chinese dataset and performs as a benchmark dataset.”
Pre-Training with Whole Word Masking for Chinese BERT (2019, unknown): “, 2018), CJRC(10) • Named Entity Recognition (NER): People Daily(11), MSRA-NER (SIGHAN 2006) (Levow, 2006) • Natural Language Inference (NLI): XNLI (Conneau et al.”
TENER: Adapting Transformer Encoder for Named Entity Recognition (2019, background): “(4) The corpus of the Chinese NER dataset MSRA came from news domain (Levow, 2006).”
Zero-Shot Information Extraction via Chatting with ChatGPT (2023, background): “ChatIE is comparable to fs-20 on MSRA, or outperforms fs-100 on NYT11-HRL, collnpp and ACE05, or even surpasses the full-shot on DuIE2.”
Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models (2023, methodology): “0 [87; 50; 96; 49; 51], and ACE05 in both languages, comparing three metrics of precision, recall, and F1 score.”
Multi-task Domain Adaptation for Sequence Tagging (2016, unknown): “…news CWS data comes from the SIGHAN 2005 shared task ( SighanCWS ) (Emerson, 2005), news NER data comes from the SIGHAN 2006 shared task ( SighanNER ) (Levow, 2006), social CWS data ( WeiboSeg ) created by Zhang et al. (2013), and social NER data ( Wei-boNER ) created by Peng and Dredze (2015).”
A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models (2023, unknown): “0 (Rajpurkar et al., 2018)), NER (ACE2005 4 , CoNLL2003 (Sang and Meulder, 2003), OntoNotesv5 5 , HONOR (Chen et al., 2023), MSRANER (Levow, 2006), and OntoNote4NER (Weischedel et al., 2013)), NLI (MNLI-m (Williams et al., 2017), MNLI-mm (Williams et al., 2017), and SNLI (Williams et al., 2017)), PO”