TNI Commander General Agus Subiyanto has high hopes for his unit, which will turn 79 years old on October 5. Agus hopes that TNI soldiers can act professionally in carrying out their duties.
“Professional soldiers, we hope that soldiers’ behavior will improve,” said Agus at the Heroes’ Cemetery, Kalibata, South Jakarta, today.
Agus emphasized that TNI soldiers must be able to provide counseling about law and human rights (HAM). This is seen as an internal effort by the TNI to improve its shortcomings.
Also read: Residents are welcome to come to the TNI’s 79th anniversary celebration at Monas, they can watch Dewa 19 and Slank for free
“Because they are well equipped, well trained. The unit commander (Dansat) provides counseling about law and human rights,” he explained.
Furthermore, Agus emphasized that all TNI soldiers must prioritize PRIMA’s vision. This vision stands for professional, responsive, integrative, modern and adaptive.
“To be a professional soldier, of course if a professional soldier has to be well-paid, so he has to have good welfare, be well-trained, have good training, be well-equipped and be well-equipped so that the soldier will be professional,” he stressed. (P-2 )
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Keyword extraction
Based on the provided web search results, it appears that you are looking for ways to extract keywords from a text. Here’s a comprehensive overview of the methods and approaches mentioned in the search results.
Method 1: Semantic Approach
According to an article on OnCrawl [[1]], semantic methods can be used to automatically extract concepts and keywords from a text. This approach involves using natural language processing (NLP) techniques to analyze the meaning and context of the text.
Method 2: Using a Large Language Model (LLM)
A GitHub issue on KeyLLM keyword extraction [[2]] suggests that you can use a large language model (LLM) to extract keywords from a text. By tweaking the prompt, you can ask the LLM to only extract keywords that are literally found in the text and not to come up with different ones.
Method 3: Using Spark NLP with Python
An article on John Snow Labs [[3]] provides an example of using Spark NLP with Python to extract keywords from a text. This approach involves using the Spark NLP library to unleash the potential of your texts and extract keywords from any text.
there are at least three methods to extract keywords from a text: semantic approach, using a large language model (LLM), and using Spark NLP with Python. Each method has its own strengths and weaknesses, and the choice of method depends on the specific use case and requirements.
References:
[[1]] Garaud, D. (2022). Automatically Extract Concepts and Keywords from a Text (Part II): A Semantic Approach. OnCrawl.
[[2]] KeyLLM keyword extraction issue #183 (2023).
[[3]] (2023). Keywords Extraction with Python & NLP. John Snow Labs.
