Chinese Named Entity Recognition
37 papers with code • 7 benchmarks • 6 datasets
Chinese named entity recognition is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc. from Chinese text (Source: Adapted from Wikipedia).
Libraries
Use these libraries to find Chinese Named Entity Recognition models and implementationsMost implemented papers
ERNIE: Enhanced Representation through Knowledge Integration
We present a novel language representation model enhanced by knowledge called ERNIE (Enhanced Representation through kNowledge IntEgration).
A Unified MRC Framework for Named Entity Recognition
Instead of treating the task of NER as a sequence labeling problem, we propose to formulate it as a machine reading comprehension (MRC) task.
ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations
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.
TENER: Adapting Transformer Encoder for Named Entity Recognition
The Bidirectional long short-term memory networks (BiLSTM) have been widely used as an encoder in models solving the named entity recognition (NER) task.
Chinese NER Using Lattice LSTM
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.
ERNIE 2.0: A Continual Pre-training Framework for Language Understanding
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.
CLUENER2020: Fine-grained Named Entity Recognition Dataset and Benchmark for Chinese
In this paper, we introduce the NER dataset from CLUE organization (CLUENER2020), a well-defined fine-grained dataset for named entity recognition in Chinese.
Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
We find empirically that the contextual representations computed on the retrieval-based input view, constructed through the concatenation of a sentence and its external contexts, can achieve significantly improved performance compared to the original input view based only on the sentence.
Glyce: Glyph-vectors for Chinese Character Representations
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.
Simplify the Usage of Lexicon in Chinese NER
This method avoids designing a complicated sequence modeling architecture, and for any neural NER model, it requires only subtle adjustment of the character representation layer to introduce the lexicon information.