SEFR CUT (Stacked Ensemble Filter and Refine for Word Segmentation)

February 2, 2024 · View on GitHub

Domain Adaptation of Thai Word Segmentation Models using Stacked Ensemble (EMNLP 2020)
CRF as Stacked Model and DeepCut as Baseline model

Read more:

Citation

@inproceedings{limkonchotiwat-etal-2020-domain,
    title = "Domain Adaptation of {T}hai Word Segmentation Models using Stacked Ensemble",
    author = "Limkonchotiwat, Peerat  and
      Phatthiyaphaibun, Wannaphong  and
      Sarwar, Raheem  and
      Chuangsuwanich, Ekapol  and
      Nutanong, Sarana",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.315",
}

Install

pip install sefr_cut

How To use

Requirements

  • python >= 3.6
  • python-crfsuite >= 0.9.7
  • pyahocorasick == 1.4.0

Example

Load Engine & Engine Mode

  • ws1000, tnhc, and BEST !!
    • ws1000: The model trained on Wisesight-1000 and test on Wisesight-160
    • tnhc: The model trained on TNHC (80:20 train&test split with random seed 42)
    • BEST: The model trained on BEST-2010 Corpus (NECTEC)
    sefr_cut.load_model(engine='ws1000')
# OR
sefr_cut.load_model(engine='tnhc')
# OR
sefr_cut.load_model(engine='best')
  • tl-deepcut-XXXX
    • We also provide transfer learning of deepcut on 'Wisesight' as tl-deepcut-ws1000 and 'TNHC' as tl-deepcut-tnhc
    sefr_cut.load_model(engine='tl-deepcut-ws1000')
# OR
sefr_cut.load_model(engine='tl-deepcut-tnhc')
  • deepcut
    • We also provide the original deepcut
    sefr_cut.load_model(engine='deepcut')

Segment Example

You need to read the paper to understand why we have kk value!

  • Tokenize with default k-value 
    sefr_cut.load_model(engine='ws1000')
print(sefr_cut.tokenize(['สวัสดีประเทศไทย','ลุงตู่สู้ๆ']))
print(sefr_cut.tokenize(['สวัสดีประเทศไทย']))
print(sefr_cut.tokenize('สวัสดีประเทศไทย'))

[['สวัสดี', 'ประเทศ', 'ไทย'], ['ลุง', 'ตู่', 'สู้', 'ๆ']]
[['สวัสดี', 'ประเทศ', 'ไทย']]
[['สวัสดี', 'ประเทศ', 'ไทย']]
  • Tokenize with a various k-value 
    sefr_cut.load_model(engine='ws1000')
print(sefr_cut.tokenize(['สวัสดีประเทศไทย','ลุงตู่สู้ๆ'],k=5)) # refine only 5% of character number
print(sefr_cut.tokenize(['สวัสดีประเทศไทย','ลุงตู่สู้ๆ'],k=100)) # refine 100% of character number

[['สวัสดี', 'ประเทศไทย'], ['ลุงตู่', 'สู้', 'ๆ']]
[['สวัสดี', 'ประเทศ', 'ไทย'], ['ลุง', 'ตู่', 'สู้', 'ๆ']]

Evaluation

  • We also provide Character & Word Evaluation by call function evaluation()
    • For example
    answer = 'สวัสดี|ประเทศไทย'
pred = 'สวัสดี|ประเทศ|ไทย'
char_score,word_score = sefr_cut.evaluation(answer,pred)
print(f'Word Score: {word_score} Char Score: {char_score}')

Word Score: 0.4 Char Score: 0.8

answer = ['สวัสดี|ประเทศไทย']
pred = ['สวัสดี|ประเทศ|ไทย']
char_score,word_score = sefr_cut.evaluation(answer,pred)
print(f'Word Score: {word_score} Char Score: {char_score}')

Word Score: 0.4 Char Score: 0.8


answer = [['สวัสดี|'],['ประเทศไทย']]
pred = [['สวัสดี|'],['ประเทศ|ไทย']]
char_score,word_score = sefr_cut.evaluation(answer,pred)
print(f'Word Score: {word_score} Char Score: {char_score}')

Word Score: 0.4 Char Score: 0.8

Performance

How to re-train the model?

  • You can re-train the model. The example is in the folder Notebooks We provided everything for you!!

    Re-train Model

    • You can run the notebook file #2, the corpus inside 'Notebooks/corpus/' is Wisesight-1000, you can try with BEST, TNHC, and LST20 !
    • Rename variable name: CRF_model_name
    • Link:HERE

    Filter and Refine Example

    • Set variable name CRF_model_name same as file#2
    • If you want to know why we use filter-and-refine, you can try to uncomment 3 lines in score_() function
    #answer = scoring_function(y_true,cp.deepcopy(y_pred),entropy_index_og)
#f1_hypothesis.append(eval_function(y_true,answer))
#ax.plot(range(start,K_num,step),f1_hypothesis,c="r",marker='o',label='Best case')

    Use your trained model?

    • Just move your model inside 'Notebooks/model/' to 'seft_cut/model/' and call model in one line.
    SEFR_CUT.load_model(engine='my_model')

Thank you many code from