BERT for Coreference Resolution: Baselines and Analysis 를 기반으로 한 인물 식별 모델입니다.
All datasets follow the CoNLL 2012 Shared Task data format. Documents are delimited by the comments in the following format:
#begin document (<Document ID>)[; part ###]
...
#end document
Each sentence is delimited by a new line ("\n") and each column indicates the following:
- Document ID:
/-(e.g.,/friends-s01e01). - Scene ID: the ID of the scene within the episode.
- Token ID: the ID of the token within the sentence.
- Word form: the tokenized word.
- Part-of-speech tag: the part-of-speech tag of the word (auto generated).
- Constituency tag: the Penn Treebank style constituency tag (auto generated).
- Lemma: the lemma of the word (auto generated).
- Frameset ID: not provided (always
_). - Word sense: not provided (always
_). - Speaker: the speaker of this sentence.
- Named entity tag: the named entity tag of the word (auto generated).
- Start time: start time of the sentence on video. (millisecond)
- End time: start time of the sentence on video. (millisecond)
- Video file: Pre-processed sequence of image file from the video corresponding to the sentence. This column represents the file name of the pickle object (Pickle object will be released on 08/01)
- Entity ID: the entity ID of the mention, that is consistent across all documents.
Here is a sample from the training dataset:
/friends-s01e01 0 0 He PRP (TOP(S(NP*) he - - Monica_Geller * 55422 59256 00005.pickle (284)
/friends-s01e01 0 1 's VBZ (VP* be - - Monica_Geller * 55422 59256 00005.pickle -
/friends-s01e01 0 2 just RB (ADVP*) just - - Monica_Geller * 55422 59256 00005.pickle -
/friends-s01e01 0 3 some DT (NP(NP* some - - Monica_Geller * 55422 59256 00005.pickle -
/friends-s01e01 0 4 guy NN *) guy - - Monica_Geller * 55422 59256 00005.pickle (284)
/friends-s01e01 0 5 I PRP (SBAR(S(NP*) I - - Monica_Geller * 55422 59256 00005.pickle (248)
/friends-s01e01 0 6 work VBP (VP* work - - Monica_Geller * 55422 59256 00005.pickle -
/friends-s01e01 0 7 with IN (PP*)))))) with - - Monica_Geller * 55422 59256 00005.pickle -
/friends-s01e01 0 8 ! . *)) ! - - Monica_Geller * 55422 59256 00005.pickle -
/friends-s01e01 0 0 C'mon VB (TOP(S(S(VP*)) c'mon - - Joey_Tribbiani * 59459 61586 00006.pickle -
/friends-s01e01 0 1 , , * , - - Joey_Tribbiani * 59459 61586 00006.pickle -
/friends-s01e01 0 2 you PRP (NP*) you - - Joey_Tribbiani * 59459 61586 00006.pickle (248)
/friends-s01e01 0 3 're VBP (VP* be - - Joey_Tribbiani * 59459 61586 00006.pickle -
/friends-s01e01 0 4 going VBG (VP* go - - Joey_Tribbiani * 59459 61586 00006.pickle -
/friends-s01e01 0 5 out RP (PRT*) out - - Joey_Tribbiani * 59459 61586 00006.pickle -
/friends-s01e01 0 6 with IN (PP* with - - Joey_Tribbiani * 59459 61586 00006.pickle -
/friends-s01e01 0 7 the DT (NP* the - - Joey_Tribbiani * 59459 61586 00006.pickle -
/friends-s01e01 0 8 guy NN *)))) guy - - Joey_Tribbiani * 59459 61586 00006.pickle (284)
/friends-s01e01 0 9 ! . *)) ! - - Joey_Tribbiani * 59459 61586 00006.pickle -
A mention may include more than one word:
/friends-s01e02 0 0 Ugly JJ (TOP(S(NP(ADJP* ugly - - Chandler_Bing * 332158 334460 00038.pickle (380
/friends-s01e02 0 1 Naked JJ *) naked - - Chandler_Bing * 332158 334460 00038.pickle -
/friends-s01e02 0 2 Guy NNP *) Guy - - Chandler_Bing * 332158 334460 00038.pickle 380)
/friends-s01e02 0 3 got VBD (VP* get - - Chandler_Bing * 332158 334460 00038.pickle -
/friends-s01e02 0 4 a DT (NP* a - - Chandler_Bing * 332158 334460 00038.pickle -
/friends-s01e02 0 5 Thighmaster NN *)) thighmaster - - Chandler_Bing * 332158 334460 00038.pickle -
/friends-s01e02 0 6 ! . *)) ! - - Chandler_Bing * 332158 334460 00038.pickle -
The mapping between the entity ID and the actual character can be found in friends_entity_map.txt
- Install python3 requirements:
pip install -r requirements.txt ./setup_all.sh: This builds the custom kernels
- data 폴더 내에 train, dev, test data set을 각각
friendstrain.english.v4_gold_conllfriendstrain.english.v4_gold_conllfriendstrain.english.v4_gold_conll로 저장 - data 폴더 내에
friends_entity_map.txt를 확인. - models 폴더 내에 BERT 모델을 다운로드.
./setup_training.sh: sh파일 내에 vocab_file 경로가 올바른지 확인 한 후 실행.- Experiment configurations을
experiments.conf에 설정. - Training:
GPU=0 python train.py <experiment>
- evaluate를 진행할 모델을 복사.
experiments.conf내에 복사된 이름의 experiment를 설정.GPU=0 python evaluate.py <experiment>.- 생성된
evaluate_result.txt로 결과 확인. python link_character_friendsnew.py,python character_evaluate_friendsnew.py실행을 통해 인물인식 성능을 확인.