Branching Entropy Tokeninzer

Branching Entropy Tokeninzer#

Developing and Evaluating a Branching Entropy-Based Subword Tokenizer for Improved Natural Language Processing in the Korean Finance and Economics Domain

Introduction#

Subword tokenization is a crucial technique in natural language processing that involves breaking down words into smaller units that capture the morphological structure of the language. In languages like Korean, where word boundaries are not well-defined and morphemes play a significant role in word structure, subword tokenization can be particularly challenging. Furthermore, in the finance and economics domain, the presence of Korean expressions of English jargon, loanwords, and technical terms that are not easily captured by standard Korean subword tokenization methods exacerbates the challenge of understanding and generating Korean texts for a language model. To address these issues, a subword tokenizer capable of effectively handling domain-specific terminology is needed.

In this research project, we propose to develop a subword tokenizer based on branching entropy for Korean in the finance and economics domain. This tokenizer will leverage a combination of domain-specific dictionaries and a general corpus of Korean text to enhance its performance. We will evaluate the tokenizer’s performance on a range of natural language processing tasks, including machine translation, text summarization, and natural language understanding, using a corpus of financial and economic text in Korean. Additionally, we will compare the performance of the branching entropy tokenizer to other subword tokenization methods to demonstrate its efficacy.

Objectives#

The main objectives of this research project are:

  1. To develop a subword tokenizer based on branching entropy for Korean in the finance and economics domain, using a combination of domain-specific dictionaries and a general corpus of Korean text, addressing the limitations of standard tokenization methods in capturing domain-specific terminology.

  2. To evaluate the performance of the tokenizer on a range of natural language processing tasks, including machine translation, text summarization, and natural language understanding, using a corpus of financial and economic text in Korean, thus demonstrating its practical utility.

  3. To compare the performance of the branching entropy tokenizer to other subword tokenization methods, including Byte Pair Encoding, the unigram model, and Morfessor, in the finance and economics domain, highlighting the benefits of the proposed method over existing approaches.

These objectives will guide our research to develop an effective subword tokenizer for the Korean finance and economics domain, ultimately improving natural language processing performance in this critical area.

Methodology#

To achieve the objectives outlined in this research, we will follow a systematic methodology that includes the development of a subword tokenizer based on branching entropy, the evaluation of its performance on various NLP tasks, and a comparison with other tokenization methods. The methodology consists of the following steps:

  1. Data Collection and Preprocessing: First, we will collect a diverse dataset of Korean financial and economic texts, including news articles, research papers, financial reports, and corporate filings, to ensure our tokenizer effectively captures domain-specific terminology. Additionally, we will gather a general corpus of Korean text to improve the tokenizer’s generalizability. All collected texts will be preprocessed to remove any irrelevant information, such as HTML tags, advertisements, and non-textual elements.

  2. Domain-Specific Dictionary Compilation: Next, we will compile a domain-specific dictionary by extracting finance and economics-related terms from the collected dataset. We will also include commonly used English jargon and technical terms that are frequently employed in the Korean finance and economics domain. This dictionary will serve as a valuable resource to guide the tokenizer in identifying domain-specific terms.

  3. Development of Branching Entropy Subword Tokenizer: We will then develop the branching entropy-based subword tokenizer for Korean finance and economics texts. Branching entropy measures the uncertainty of character sequences in a text, which can be used to identify subword boundaries. By incorporating the domain-specific dictionary and the general Korean corpus, we will train the tokenizer to identify subword units that are relevant to the finance and economics domain, while maintaining its ability to tokenize general Korean text.

  4. Evaluation of Tokenizer Performance: To assess the performance of the branching entropy tokenizer, we will apply it to various NLP tasks, such as machine translation, text summarization, and natural language understanding. We will use the finance and economics text corpus to train and test models based on our tokenizer. The evaluation metrics for each task will include BLEU scores for machine translation, ROUGE scores for text summarization, and accuracy and F1 scores for natural language understanding.

  5. Comparison with Other Subword Tokenization Methods: Finally, we will compare the performance of the branching entropy tokenizer to other subword tokenization methods, including Byte Pair Encoding (BPE), the unigram model, and Morfessor. We will apply these tokenization methods to the same NLP tasks used to evaluate the branching entropy tokenizer and compare the results using the same evaluation metrics.

By following this methodology, we aim to develop a branching entropy-based subword tokenizer that effectively handles Korean finance and economics texts while demonstrating its superiority to existing tokenization methods.

Expected Outcomes#

Through the systematic implementation of the proposed methodology, we anticipate the following outcomes for this research project:

  1. Effective Subword Tokenizer: We expect to develop a branching entropy-based subword tokenizer that effectively handles Korean finance and economics texts by identifying domain-specific terminology and morphological structures, while maintaining its ability to process general Korean text.

  2. Improved NLP Task Performance: By employing the developed tokenizer, we anticipate an improvement in the performance of various NLP tasks, such as machine translation, text summarization, and natural language understanding, in the Korean finance and economics domain. This would be evidenced by higher evaluation scores (e.g., BLEU, ROUGE, accuracy, and F1) compared to models trained without the proposed tokenizer.

  3. Superiority over Existing Tokenization Methods: We expect our branching entropy-based tokenizer to demonstrate superior performance when compared to other subword tokenization methods, such as Byte Pair Encoding, the unigram model, and Morfessor, in the context of the finance and economics domain. This would be evidenced by better evaluation scores for the same NLP tasks when using our tokenizer.

  4. Expanded Knowledge and Applications: The successful development and evaluation of the proposed tokenizer will expand our understanding of subword tokenization methods and their applications in domain-specific NLP tasks, particularly for morphologically complex languages like Korean. This research could potentially inspire further studies and innovations in tokenizer development and NLP for other languages and domains.

  5. Practical Implications: The developed tokenizer will have practical implications for various industries in the finance and economics domain, such as banking, financial analysis, and investment management, by improving the efficiency and accuracy of automated language processing tasks, including machine translation of financial documents, summarization of economic reports, and natural language-based data analysis.

Conclusion#

In this research project, we have proposed the development and evaluation of a branching entropy-based subword tokenizer for Korean finance and economics texts. By effectively capturing domain-specific terminology and morphological structures, we anticipate that our tokenizer will improve the performance of various natural language processing tasks, such as machine translation, text summarization, and natural language understanding, in the Korean finance and economics domain. Moreover, we expect our tokenizer to demonstrate superiority over existing subword tokenization methods, further highlighting its potential contributions to the field of NLP.

The successful completion of this research will not only expand our knowledge of subword tokenization methods and their applications in domain-specific NLP tasks but also have practical implications for various industries in the finance and economics domain. As natural language processing continues to advance and become increasingly relevant in diverse fields, the development of effective domain-specific tokenizers like the one proposed in this research will play a vital role in enhancing the efficiency and accuracy of automated language processing tasks, ultimately benefiting both academia and industry.

References#

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