distributed representation

Oct 13, 2025 by miodeya.com 127 views

Pre-Trained Word Vector Indonesian Generation Using Word2VEC for Computer and Information Technology Fields

Introduction

In the field of natural language processing (NLP), making an effective representation of words is crucial for various tasks. One popular method is word embedding, which represents words with high-dimensional vectors. This method is more efficient than traditional methods such as "Bag of Words" because it is able to capture the meaning and relationship between words. Another advantage of distributed representation is that it produces Word Vectors that contain semantic and syntactic information, so that words with close meanings will have a vector that is also close.

However, the process of making word embedding requires a large dataset and long training time. To overcome this, many researchers have developed pre-trained word vectors that can be reused. Unfortunately, the pre-trained word vectors available are usually only for general domains, and are not necessarily optimal for special domains such as computers and information technology.

Background

The development of pre-trained word vectors has become increasingly important in the field of NLP. These vectors can be used as a starting point for various NLP applications, such as text classification, information search, and machine translation. However, the existing pre-trained word vectors are not specifically designed for computer and information technology domains.

To address this gap, this study aims to create a special pre-trained word vector for computer and information technology. The dataset used comes from the repository of the scientific work of the University of North Sumatra (USU). The selected method is Word2VEC, which has two architectures:

*** Continuous Bag-of-Words (CBOW): ** This method predicts the word target based on its context (the words around it). *** Skip-gram: ** This method predicts words around the target word based on the word target itself.

Methodology

The methodology used in this study involves the following steps:

Data Collection: The dataset used comes from the repository of the scientific work of the University of North Sumatra (USU).
Preprocessing: The dataset is preprocessed to remove any unnecessary characters and to convert all text to lowercase.
Word2VEC Training: The preprocessed dataset is then used to train the Word2VEC model using the CBOW and skip-gram methods.
Evaluation: The resulting pre-trained word vectors are evaluated using various metrics, such as accuracy and precision.

Results

The results of this study show that the Word2VEC model with the CBOW method was more effective than the skip-gram method in producing pre-trained word vectors for computer domains and information technology. This is probably because CBOW is more suitable for capturing the context and relationship between words in scientific texts, which tend to have a more complex sentence structure.

Discussion

The pre-trained word vectors resulting from this study can be useful for various NLP applications in the field of computer and information technology, such as:

*** Text Classification: ** Classifying documents based on topics, such as software development, cyber security, or artificial intelligence. *** Information Search: ** Increasing the relevance of search results by understanding the meaning of words in the query. *** Machine Translation: ** Improving the quality of translation by understanding the meaning of words in the context of information technology.

Conclusion

This study opens opportunities for the development of pre-trained word vectors which are more specific to various domains. By using relevant datasets and the right method, we can create a more accurate and useful word representation for various NLP applications.

Future Work

Future work can involve the following:

Extending the Dataset: The dataset used in this study can be extended to include more texts from various sources.
Using Other Methods: Other methods, such as GloVe and FastText, can be used to compare the results with Word2VEC.
Applying to Other Domains: The pre-trained word vectors can be applied to other domains, such as medicine and finance.

References

Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed Representations of Words and Phrases and their Compositionality. Advances in Neural Information Processing Systems, 26, 3111-3119.
Pennington, J., Socher, R., & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, 1532-1543.
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics, 5, 135-146.

Appendices

Appendix A: The dataset used in this study.
Appendix B: The code used to train the Word2VEC model.
Appendix C: The results of the evaluation of the pre-trained word vectors.
Q&A: Pre-Trained Word Vector Indonesian Generation Using Word2VEC for Computer and Information Technology Fields

Frequently Asked Questions

Q: What is the purpose of this study?

A: The purpose of this study is to create a special pre-trained word vector for computer and information technology domains using the Word2VEC method.

Q: What is Word2VEC?

A: Word2VEC is a popular method for creating word embeddings, which represent words as high-dimensional vectors. This method is more efficient than traditional methods such as "Bag of Words" because it is able to capture the meaning and relationship between words.

Q: What are the advantages of using pre-trained word vectors?

A: Pre-trained word vectors can be reused in various NLP applications, such as text classification, information search, and machine translation. They can also reduce the training time and improve the accuracy of NLP models.

Q: What are the challenges of creating pre-trained word vectors?

A: One of the challenges of creating pre-trained word vectors is the need for a large dataset and long training time. Additionally, the existing pre-trained word vectors are not specifically designed for special domains such as computers and information technology.

Q: What is the dataset used in this study?

A: The dataset used in this study comes from the repository of the scientific work of the University of North Sumatra (USU).

Q: What are the two architectures used in Word2VEC?

A: The two architectures used in Word2VEC are:

Q: Which method is more effective in producing pre-trained word vectors for computer domains and information technology?

A: The results of this study show that the Word2VEC model with the CBOW method is more effective than the skip-gram method in producing pre-trained word vectors for computer domains and information technology.

Q: What are the potential applications of the pre-trained word vectors resulting from this study?

A: The pre-trained word vectors resulting from this study can be useful for various NLP applications in the field of computer and information technology, such as:

Q: What are the future directions of this study?

A: Future work can involve extending the dataset, using other methods, and applying the pre-trained word vectors to other domains.

Additional Questions and Answers

Q: How can I use the pre-trained word vectors resulting from this study?

A: You can use the pre-trained word vectors as a starting point for various NLP applications, such as text classification, information search, and machine translation.

Q: Can I use the pre-trained word vectors for other languages?

A: The pre-trained word vectors resulting from this study are specifically designed for Indonesian language. However, you can use the same method to create pre-trained word vectors for other languages.

Q: How can I contribute to this study?

A: You can contribute to this study by providing feedback, suggesting new ideas, or participating in the development of the pre-trained word vectors.

Conclusion

This Q&A article provides an overview of the study on pre-trained word vector Indonesian generation using Word2VEC for computer and information technology fields. It answers frequently asked questions and provides additional information on the study.