Evaluating semantic similarity using sentence embeddings
Abstract: Semantic similarity search is the task of searching for documents or sentences which contain semantically similar content to a user-submitted search term. This task is often carried out, for instance when searching for information on the internet. To facilitate this, vector representations referred to as embeddings of both the documents to be searched as well as the search term must be created. Traditional approaches to create embeddings include the term frequency - inverse document frequency algorithm (TF-IDF). Modern approaches include neural networks, which have seen a large rise in popularity over the last few years. The BERT network released in 2018 is a highly regarded neural network which can be used to create embeddings. Multiple variations of the BERT network have been created since its release, such as the Sentence-BERT network which is explicitly designed to create sentence embeddings. This master thesis is concerned with evaluating semantic similarity search using sentence embeddings produced by both traditional and modern approaches. Different experiments were carried out to contrast the different approaches used to create sentence embeddings. Since datasets designed explicitly for the types of experiments performed could not be located, commonly used datasets were modified. The results showed that the TF-IDF algorithm outperformed the neural network based approaches in almost all experiments. Among the neural networks evaluated, the Sentence-BERT network performed proved to be better than the BERT network. To create more generalizable results, datasets explicitly designed for the task are needed.
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