amr
amr¶
AMR captures “who is doing what to whom” in a sentence. Each sentence is represented as a rooted, directed, acyclic graph with labels on edges (relations) and leaves (concepts).
Before loading an AMR model, make sure to install HanLP with the amr dependencies:
pip install hanlp[amr] -U
To parse a raw sentence into AMR:
import hanlp
amr_parser = hanlp.load(hanlp.pretrained.amr.AMR3_SEQ2SEQ_BART_LARGE)
amr = amr_parser('The boy wants the girl to believe him.')
print(amr)
(z1 / want-01
:ARG0 (z2 / boy)
:ARG1 (z3 / believe-01
:ARG0 (z4 / girl)
:ARG1 z2))
All the pre-trained parsers and their scores are listed below.
- hanlp.pretrained.amr.AMR3_GRAPH_PRETRAIN_PARSER = 'https://file.hankcs.com/hanlp/amr/amr3_graph_pretrain_parser_20221207_153759.zip'¶
A seq2seq (Bevilacqua et al. 2021) BART (Lewis et al. 2020) large parser trained on Abstract Meaning Representation 3.0 (Knight et al. 2014) with graph pre-training (Bai et al. 2022). Its performance is
84.3according to their official repository. Usingamr-evaluation-enhanced, the performance is slightly lower:Metric
P
R
F1
Smatch
84.4
83.6
84.0
Unlabeled
86.7
85.8
86.2
No WSD
84.9
84.1
84.5
Non_sense_frames
91.8
91.6
91.7
Wikification
83.6
81.7
82.6
Named Ent.
89.3
87.4
88.4
Negations
71.6
72.2
71.9
IgnoreVars
74.6
74.2
74.4
Concepts
90.7
90.0
90.3
Frames
88.8
88.5
88.7
Reentrancies
72.1
72.9
72.5
SRL
80.1
80.7
80.4
Note this parser does NOT perform wikification.
- hanlp.pretrained.amr.AMR3_SEQ2SEQ_BART_LARGE = 'https://file.hankcs.com/hanlp/amr/amr3_seq2seq_bart_large_83.30_20220125_114450.zip'¶
A seq2seq (Bevilacqua et al. 2021) BART (Lewis et al. 2020) large parser trained on Abstract Meaning Representation 3.0 (Knight et al. 2014). Its performance is
Metric
P
R
F1
Smatch
84.00
82.60
83.30
Unlabeled
86.40
84.90
85.70
No WSD
84.50
83.10
83.80
Non_sense_frames
91.90
91.30
91.60
Wikification
81.70
80.80
81.20
Named Ent.
89.20
87.00
88.10
Negations
71.70
70.90
71.30
IgnoreVars
73.80
73.10
73.50
Concepts
90.70
89.60
90.10
Frames
88.50
87.90
88.20
Reentrancies
70.40
71.80
71.10
SRL
79.00
79.60
79.30
Note this parser does NOT perform wikification.
- hanlp.pretrained.amr.MRP2020_AMR_ENG_ZHO_XLM_BASE = 'http://download.hanlp.com/amr/extra/amr-eng-zho-xlm-roberta-base_20220412_223756.zip'¶
A wrapper for the Permutation-invariant Semantic Parser (Samuel & Straka 2020) trained on MRP2020 English and Chinese AMR corpus. It was ranked the top in the MRP2020 competition, while this release is a base version. See the original paper for the detailed performance. Note this model requires tokens and lemmas (for English) to be provided as inputs.
- hanlp.pretrained.amr.MRP2020_AMR_ZHO_MENGZI_BASE = 'http://download.hanlp.com/amr/extra/amr-zho-mengzi-base_20220415_101941.zip'¶
A Chinese Permutation-invariant Semantic Parser (Samuel & Straka 2020) trained on MRP2020 Chinese AMR corpus using Mengzi BERT base (Zhang et al. 2021). Its performance on dev set is
{amr-zho [tops F1: 85.43%][anchors F1: 93.41%][labels F1: 87.68%][properties F1: 82.02%][edges F1: 73.17%] [attributes F1: 0.00%][all F1: 84.11%]}. Test set performance is unknown since the test set is not released to the public.