***** [Update] November, 2019 *****
Kaiser is available for both of English FrameNet and Korean FrameNet
Kaiser is a semantic parser to understand the meaning of texts in terms of FrameNet.
frame (frame semantics) is a schematic representation of a situation or an event.
For an example sentence, '헤밍웨이는 1899년 7월 21일 일리노이에서 태어났고, 62세에 자살로 사망했다.', KAIST-frame-parser identifies several frames such as Being_born and Death for Korean lexical units (e.g. 태어나다.v and 사망하다.v)
Our model is based on the BERT with fine-tuning. The model predict Frames and their arguments jointly.
Install
Install transformers, kaiser, and Korean FrameNet
pip install transformers
git clone https://github.com/machinereading/kaiser.git
cd ./kaiser
git clone https://github.com/machinereading/koreanframenet.git
Download the pretrained model
Download two pretrained model files to {your_model_dir} (e.g. /home/model/bert_ko_srl_model.pt).
Import model (in your python code) (make sure that your code is in a parent folder of kaiser)
from kaiser import kaiser
model_path = {your_model_dir} # absolute_path (e.g. /home/model/bert_ko_frame_model.pt)
parser = parser.ShallowSemanticParser(model_path=model_path, masking=True)
optional: If you want to DO NOT USE LU DICTIONARY, set argument masking=False)
Parse the input text
text = '헤밍웨이는 1899년 7월 21일 미국 일리노이에서 태어났고 62세에 자살로 사망했다.'
parsed = parser.parser(text, sent_id='1', result_format='all')
optional: sent_id and result_format is not mandatory argument.
You can get the result in following argument: conll', graph, textae, and all.
The result consits of following three parts:
(1) triple format (result_format='graph')
(2) conll format (result_format='conll')
(3) pubannotation format (result_format='textae')
Or, you can get all result in json by result_format='all'
result
conll format The result is a list, which consists of multiple Frame-Semantic structures. Each SRL structure is in a list, which consists of four lists: (1) tokens, (2) lexical units, (3) its frames, and (4) its arguments. For example, for the given input text, the output is in the following format:
[
[
['헤밍웨이는', '1899년', '7월', '21일', '미국', '일리노이에서', '태어났고,', '62세에', '자살로', '사망했다.'],
['_', '_', '_', '_', '미국.n', '_', '_', '_', '_', '_'],
['_', '_', '_', '_', 'Origin', '_', '_', '_', '_', '_'],
['O', 'O', 'O', 'O', 'O', 'B-Entity', 'O', 'O', 'O', 'O']
],
[
['헤밍웨이는', '1899년', '7월', '21일', '미국', '일리노이에서', '태어났고,', '62세에', '자살로', '사망했다.'],
['_', '_', '_', '_', '_', '_', '태어나다.v', '_', '_', '_'],
['_', '_', '_', '_', '_', '_', 'Being_born', '_', '_', '_'],
['B-Child', 'B-Time', 'I-Time', 'I-Time', 'B-Place', 'I-Place', 'O', 'O', 'O', 'O']
],
[
['헤밍웨이는', '1899년', '7월', '21일', '미국', '일리노이에서', '태어났고,', '62세에', '자살로', '사망했다.'],
['_', '_', '_', '_', '_', '_', '_', '_', '자살.n', '_'],
['_', '_', '_', '_', '_', '_', '_', '_', 'Killing', '_'],
['B-Victim', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']
],
[
['헤밍웨이는', '1899년', '7월', '21일', '미국', '일리노이에서', '태어났고,', '62세에', '자살로', '사망했다.'],
['_', '_', '_', '_', '_', '_', '_', '_', '_', '사망.n'],
['_', '_', '_', '_', '_', '_', '_', '_', '_', 'Death'],
['B-Protagonist', 'O', 'O', 'O', 'O', 'O', 'O', 'B-Time', 'B-Manner', 'O']
]
]
Another example sentence is '그는 그녀와 사랑에 빠졌다.'.
[
[
['그는', '그녀와', '사랑에', '빠졌다.'],
['_', '_', '사랑.n', '_'],
['_', '_', 'Personal_relationship', '_'],
['B-Partner_1', 'B-Partner_2', 'O', 'O']
],
[
['그는', '그녀와', '사랑에', '빠졌다.'],
['_', '_', '_', '빠지다.v'],
['_', '_', '_', 'Experiencer_focus'],
['B-Experiencer', 'B-Topic', 'I-Topic', 'O']
]
]
The word '빠지다' would be have different meaning in its usage in the context.
An example is '검은 얼룩이 흰 옷에서 빠졌다.'.
[
[
['검은', '얼룩이', '흰', '옷에서', '빠졌다.'],
['_', '_', '_', '옷.n', '_'],
['_', '_', '_', 'Clothing', '_'],
['O', 'O', 'B-Descriptor', 'O', 'O']
],
[
['검은', '얼룩이', '흰', '옷에서', '빠졌다.'],
['_', '_', '_', '_', '빠지다.v'],
['_', '_', '_', '_', 'Emptying'],
['B-Theme', 'I-Theme', 'B-Source', 'I-Source', 'O']
]
]
CC BY-NC-SAAttribution-NonCommercial-ShareAlike- If you want to commercialize this resource, please contact to us
Machine Reading Lab @ KAIST
Younggyun Hahm. hahmyg@kaist.ac.kr, hahmyg@gmail.com
This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (2013-0-00109, WiseKB: Big data based self-evolving knowledge base and reasoning platform)