wehub-resource-sync
62 lines
1.3 KiB
Markdown
62 lines
1.3 KiB
Markdown
---
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jupytext:
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formats: ipynb,md:myst
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text_representation:
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extension: .md
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format_name: myst
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format_version: '0.8'
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jupytext_version: 1.4.2
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kernelspec:
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display_name: Python 3
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language: python
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name: python3
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---
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# constituency
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Constituency Parsing is the process of analyzing the sentences by breaking down it into sub-phrases also known as constituents.
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To parse a tokenized sentence into constituency tree, first load a parser:
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```{eval-rst}
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.. margin:: Batching is Faster
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.. Hint:: To speed up, parse multiple sentences at once, and use a GPU.
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```
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```{code-cell} ipython3
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:tags: [output_scroll]
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import hanlp
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con = hanlp.load(hanlp.pretrained.constituency.CTB9_CON_FULL_TAG_ELECTRA_SMALL)
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```
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Then parse a sequence or multiple sequences of tokens to it.
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```{code-cell} ipython3
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:tags: [output_scroll]
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tree = con(["2021年", "HanLPv2.1", "带来", "最", "先进", "的", "多", "语种", "NLP", "技术", "。"])
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```
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The constituency tree is a nested list of constituencies:
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```{code-cell} ipython3
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:tags: [output_scroll]
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tree
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```
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You can `str` or `print` it to get its bracketed form:
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```{code-cell} ipython3
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:tags: [output_scroll]
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print(tree)
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```
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All the pre-trained parsers and their scores are listed below.
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```{eval-rst}
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.. automodule:: hanlp.pretrained.constituency
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:members:
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``` |