Unveiling parts of Urdu Language

Language can be both simple and complex. After all, a sentence consists of a string of unruly words. There can be many types of parts that make the structure of Urdu text just like in English but there is also a need of a structured way to understand them. This is where some Artificial Intelligence comes into play. The goal is to understand parts of the different components of the sentence. Not only this but AI also explores properties of the content and its sequence. It easily understands the sequence these parts appear in. Then AI learns to connect these parts correctly to make sentences. This helps AI tounderstand more information given the text. How does this help? Well, it allows machines to classify every word in a sentence in a language like Urdu. AI is improved by a method called CRF. CRF or Conditional Random Fields is a type of probabilistic graph. It ranks possible sequences of words in sentences. This makes the CRF very good at predicting each part of the word. The CRF works by considering the context around a word. However, it s not perfect. CRF only works well when it is trained with a large amount of data. Something Urdu is lacking. This means that, there will be many challenges AI has to face. Many researchers struggle with the same problem. They have to deal with not getting enough data and words in Urdu that can make multiple sentences. Even with all this, a model has been set up with the goal of understanding and labeling each word. This is done with language-independent properties of Urdu text. It uses words present in sentences to guess what the word can be. It works across different Urdu text projects This is known as the MM-POST. The MM-POST consists of 119, 276 URDU pieces of information from seven domains. These domains are Entertainment, Finance, General, Health, Politics, Science, and Sports. This can be a lot of data. However the model can still be challenged by the fact that it is hard to classify exactly what each word is. Some developments claim to be superior to previous methods. Something surprising with all of this is that the CRF method could accurately predict what each word in a sentence was using only a small amount of information from the sentence. Also this model, the CRF model, has shown different results when tested on different samples. What does this mean? It means that these methods suffer from the problem of overfitting. This is when a model is trained well on a small sample but fails when tested on a larger sample of words. However, the CRF method develops ways for a good performance when dealing with both small and large data samples. So it has to be careful. There are many question marks about this. For example, what makes a sentence in a language understandable? This is a good question. In the same way CRF achieves high accuracy by analyzing data. AI needs to understand words and their properties. And one major question to think about is that the AI model remains accurate in the real world. Given this choice it must be kept simple. But always remember, CRF or any AI model can't be expected to be able to make any word understandable. There has to be balance between the features and the data it is exposed to. Keep in mind that AI is still in the process of learning.