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Recently, pre-trained models have achieved state-of-the-art results in various language understanding tasks, which indicates that pre-training on large-scale corpora may play a crucial role in natural language processing.
CHINESE NAMED ENTITY RECOGNITION CHINESE READING COMPREHENSION CHINESE SENTENCE PAIR CLASSIFICATION CHINESE SENTIMENT ANALYSIS LINGUISTIC ACCEPTABILITY MULTI-TASK LEARNING NATURAL LANGUAGE INFERENCE OPEN-DOMAIN QUESTION ANSWERING SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS
We present a novel language representation model enhanced by knowledge called ERNIE (Enhanced Representation through kNowledge IntEgration).
#2 best model for Chinese Sentence Pair Classification on LCQMC Dev
CHINESE NAMED ENTITY RECOGNITION CHINESE SENTENCE PAIR CLASSIFICATION CHINESE SENTIMENT ANALYSIS NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC SIMILARITY SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS
We investigate a lattice-structured LSTM model for Chinese NER, which encodes a sequence of input characters as well as all potential words that match a lexicon.
#4 best model for Chinese Named Entity Recognition on Weibo NER
Moreover, it is shown that reasonable performance can be obtained when ZEN is trained on a small corpus, which is important for applying pre-training techniques to scenarios with limited data.
SOTA for Chinese Word Segmentation on MSR
In this paper, we introduce the NER dataset from CLUE organization (CLUENER2020), a well-defined fine-grained dataset for named entity recognition in Chinese.
However, due to the lack of rich pictographic evidence in glyphs and the weak generalization ability of standard computer vision models on character data, an effective way to utilize the glyph information remains to be found.
CHINESE DEPENDENCY PARSING CHINESE NAMED ENTITY RECOGNITION CHINESE PART-OF-SPEECH TAGGING CHINESE SEMANTIC ROLE LABELING CHINESE SENTENCE PAIR CLASSIFICATION CHINESE WORD SEGMENTATION DEPENDENCY PARSING DOCUMENT CLASSIFICATION IMAGE CLASSIFICATION LANGUAGE MODELLING MACHINE TRANSLATION MULTI-TASK LEARNING PART-OF-SPEECH TAGGING SEMANTIC ROLE LABELING SEMANTIC TEXTUAL SIMILARITY SENTENCE CLASSIFICATION SENTIMENT ANALYSIS
However, existing methods for Chinese NER either do not exploit word boundary information from CWS or cannot filter the specific information of CWS.
The Bidirectional long short-term memory networks (BiLSTM) have been widely used as an encoder in models solving the named entity recognition (NER) task.
#4 best model for Chinese Named Entity Recognition on Resume NER
A bottleneck problem with Chinese named entity recognition (NER) in new domains is the lack of annotated data.