Detect the language of a text input.

The question seeks to examine your technical understanding and experience in implementing language detection systems. You're expected to describe how you would design a system that is capable of accurately detecting the language of a given text input, regardless of the language's complexity or the variety in the given text input.

To effectively answer this question, consider the basic components of a language detection system:

1. Text Preprocessing: Cleaning the input text and making modifications such as normalization.

2. Model or Algorithm: Applying machine learning algorithms or models such as Naive Bayes, Support Vector Machine (SVM), or Natural Language Processing (NLP) models.

3. Post-processing: Checking the output and ensuring it's in the desired format.

4. Evaluation: Testing the system's accuracy and efficiency.

5. Training Data: You need an adequate amount of data in different languages to train your system.

In developing a language detection system, the first step would be text preprocessing. This stage would include tokenization - breaking the text into words or tokens. We also need to address issues such as punctuations and case convertions (to lower case for example.

The next step involves choosing a suitable detection model. The Naive Bayes classifier would be a good option here due to its simplicity and high efficiency in text classification tasks. In language detection, the Naive Bayes classifier works by relating each word to a specific language using the probability that a word appears in that language.

Lastly, we will evaluate the system's performance. We could split our dataset into a training set and a test set. Once the model is trained, we can test its accuracy on the test set.

Another approach to design a language detection system may include building a character-based N-gram model. After preprocessing the text, the next step would be to generate N-grams from the text. N-grams are contiguous sequences of N items from the given text. We then calculate the frequency of each N-gram.

To classify a language, we would compare the N-gram frequencies of the unknown text with the frequencies in our trained N-gram language models. The language of the text would be identified based on the highest similarity score.

Evaluation of the system would involve using different languages and dialect texts to test the detection accuracy and robustness of the system.