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

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

Read more:

• Paper: Domain Adaptation of Thai Word Segmentation Models using Stacked Ensemble
• Blog: ตัดคำภาษาไทยและภาษาอื่นไปถึงไหนกันแล้ว?
• New verision (ACL2021): OSKut (Out-of-domain StacKed cut for Word Segmentation)

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

• Example files are on SEFR Example notebook
• Try it on Colab

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 $k$ 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')
  

  • Link:HERE

  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

• Deepcut (Baseline Model) : We used some of code from Deepcut to perform transfer learning
• @bact (CRF training code) : We used some from https://github.com/bact/nlp-thai