# UnifiedCut: A Simple and Efficient Neural Model for Thai, Burmese and Khmer Word Segmentation
## 4 Experiments
### Datasets and Evaluation Metrics  
For Thai word segmentation tasks, we evaluate the proposed method using the InterBEST2010 [27] and LST20 [28] datasets, which were both annotated by Thailand's National Electronics and Computer Technology Center (NECTEC). The BEST-2010 corpus consists of 34,107 samples (rows) across 415 documents in four categories: articles, encyclopedias, news, and novels. We reserve 10% of the training samples from each category as validation data. The LST20 corpus includes 74,180 sentences across 4751 documents spanning fifteen categories with 3794 documents in the training set, 474 in the validation set, and 483 in the test set.  
For Burmese word segmentation tasks, we use the ALT corpus ([[https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/](https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/)]([https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/](https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/)), accessed on 1 December 2024) from the Asian Language Treebank (ALT) project [29]. Briefly, 20,000 English sentences collected from Wikinews were translated manually into different Asian languages as the raw data. We divide the Burmese data into training, validation, and test sets according to 8:1:1.  
For Khmer word segmentation tasks, we use the khPOS dataset [3] which is published on GitHub ([[https://github.com/ye-kyaw-thu/khPOS](https://github.com/ye-kyaw-thu/khPOS)]([https://github.com/ye-kyaw-thu/khPOS](https://github.com/ye-kyaw-thu/khPOS)), accessed on 1 December 2024). The khPOS dataset consists of 12,000 sentences (25,626 words in total). The average number of words per sentence is 10.75. We divide the 12,000-sentence dataset into training and validation sets according to 9:1. Thu [3] provided a separate open test set for evaluating the model performance. Table 1 shows statistical information about the experiment corpus.  
Following AttaCut [5], we use precision, recall, and F1-score as evaluation metrics, which were calculated at both the character level and word level for word segmentation. Word-level precision is defined as the ratio of correctly segmented words to the total number of predicted words, while word-level recall is the ratio of correctly segmented words to the total number of words in the ground truth. \(F1_{\text{w}}\) denotes the word level F1-score. We also assess the recall for out-of-vocabulary (OOV) words to highlight the model's ability to generalize. We use the PyThaiNLP project's benchmark module for word segmentation to  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Language**} & \multicolumn{3}{c}{**Train**} & \multicolumn{3}{c}{**Validation**} & \multicolumn{3}{c}{**Test**} \\ \cline{2-10}  & **Sentence** & **Word** & **Char** & **Sentence** & **Word** & **Char** & **Sentence** & **Word** & **Char** \\ \hline BEST-2010 & 134,096 & 5105 K & 18,693 K & 14,899 & 565 K & 2069 K & 2252 & 125.2 K & 447 K \\ LST20 & 63,310 & 2715 K & 10,273 K & 5620 & 241 K & 945.9 K & 5250 & 207.3 K & 780.1 K \\ ALT & 16,085 & 608 K & 2682 K & 2011 & 74.8 K & 335 K & 2010 & 74.8 K & 335 K \\ KhPOS & 10,800 & 115 K & 545 K & 1200 & 13.5 K & 64 K & 1000 & 10.7 K & 51 K \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed statistics of experimental datasets for Thai, Burmese, and Khmer word segmentation tasks.  
compute these metrics ([[https://github.com/PyThaiNLP/pythainlp/commit/bec416f9b97](https://github.com/PyThaiNLP/pythainlp/commit/bec416f9b97) fe4]([https://github.com/PyThaiNLP/pythainlp/commit/bec416f9b97](https://github.com/PyThaiNLP/pythainlp/commit/bec416f9b97) fe4) 198c4f4288 df517696557b475e, accessed on 1 December 2024).