Determine if two sentences are similar.

This question is asking you to explain an algorithm that can be used to identify the similarity between two sentences. You're expected to give an understandable explanation of how the algorithm identifies the similarities and outline the steps it takes to do so. The interviewer is looking to assess your understanding and application of algorithms and data structures. For this question, key considerations include Natural Language Processing (NLP) techniques, string manipulation, tokenizing and vectorizing text and applications of cosine similarity or other statistical methods for similarity measurement.

One commonly used metric to determine the similarity between two sentences is the Cosine Similarity. The process can be broken down into a few steps:

1. Tokenization: First, the algorithm tokenizes both sentences. This step involves converting the sentences into a series of words or tokens.

2. Vectorization: The next step is vectorization, where each unique word is represented as a vector in n-dimensional space where n is the total number of unique words in our two sentences.

3. Cosine Similarity Calculation: The final step is to calculate the cosine of the angle between these two vectors. If the cosine value is close to 1, that means the angle between the vectors is small, which in turn means the sentences are very similar. Conversely, if the cosine value is close to -1, the sentences are dissimilar.

Another strategy is the use of a language model like BERT (Bidirectional Encoder Representations from Transformers), which can be used to calculate sentence similarity. BERT is a transformer-based machine learning technique for NLP pretraining developed by Google.

The steps are:

1. Tokenization: The sentences are tokenized, split into words or sub-words.

2. Embeddings calculation: BERT representations are then calculated for each of the tokens. These representations capture the semantic meaning of the words.

3. Aggregation: The embeddings of all tokens in a sentence are averaged to get a vector that represents the sentence.

4. Cosine Similarity Calculation: Finally, the cosine similarity between vectors of the two sentences is calculated. If the value is closer to 1, the sentences are similar, and if it's closer to -1, they are dissimilar.

While the BERT model does require more computational resources than a simple cosine similarity approach, it is a lot more accurate in capturing the semantic similarity as it is context-aware and captures both syntactic and semantic information unlike the traditional bag of words approach.