108 lines
5.1 KiB
ReStructuredText
108 lines
5.1 KiB
ReStructuredText
Start
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=====
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Model Framework
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---------------
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.. image:: ./_static/architectures.png
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DeepKE contains three modules for named entity recognition, relation extraction and attribute extraction, the three tasks respectively.
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Each module has its own submodules. For example, there are standard, document-level and few-shot submodules in the attribute extraction modular.
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Each submodule compose of three parts: a collection of tools, which can function as tokenizer, dataloader, preprocessor and the like, a encoder and a part for training and prediction
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Dataset
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-------
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We use the following datasets in our experiments:
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+--------------------------+-----------+------------------+----------+------------+
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| Task | Settings | Corpus | Language | Model |
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+==========================+===========+==================+==========+============+
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| | | CoNLL-2003 | English | |
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| | Standard +------------------+----------+ BERT |
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| | | People's Daily | Chinese | |
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| +-----------+------------------+----------+------------+
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| | | CoNLL-2003 | | |
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| | +------------------+ | |
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| Name Entity Recognition | | MIT Movie | | |
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| | Few-shot +------------------+ English | LightNER |
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| | | MIT Restaurant | | |
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| | +------------------+ | |
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| | | ATIS | | |
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+--------------------------+-----------+------------------+----------+------------+
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| | | | | CNN |
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| | | | +------------+
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| | | | | RNN |
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| | | | | Capsule |
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| | Standard | DuIE | Chinese +------------+
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| | | | | GCN |
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| | | | +------------+
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| | | | | Transformer|
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| | | | +------------+
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| | | | | BERT |
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| +-----------+------------------+----------+------------+
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| Relation Extraction | | SEMEVAL(8-shot) | | |
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| | +------------------+ | |
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| | | SEMEVAL(16-shot) | | |
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| | Few-shot +------------------+ English | KnowPrompt |
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| | | SEMEVAL(32-shot) | | |
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| | +------------------+ | |
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| | | SEMEVAL(Full) | | |
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| +-----------+------------------+----------+------------+
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| | | DocRED | | |
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| | +------------------+ | |
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| | Document | CDR | English | DocuNet |
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| | +------------------+ | |
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| | | GDA | | |
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+--------------------------+-----------+------------------+----------+------------+
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| | | | | CNN |
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| | | | | RNN |
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| | |Triplet Extraction| | Capsule |
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| Attribute Extraction | Standard |Dataset | Chinese +------------+
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| | | | | GCN |
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| | | | +------------+
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| | | | | Transformer|
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| | | | | BERT |
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+--------------------------+-----------+------------------+----------+------------+
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Get Start
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---------
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If you want to use our code , you can do as follow:
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.. code-block:: python
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git clone https://github.com/zjunlp/DeepKE.git
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cd DeepKE
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