#Jaccard similarity python code
To gain a better understanding of the two ways we evaluate text similarity, let’s use code the example above in python. I covered the Euclidean Distance and Cosine Similarity in Vector Space Models, and Sanket Gupta’s article on an Overview of Text Similarity Metrics covers the Jaccard similarity metric in good detail. Metrics provide us with objective and informative feedback to evaluate a task. “The documents are pretty similar” is subject and not very informative in comparison to the model has a 90% accuracy score. Whenever we are performing some sort of Natural Language Processing task, we need a way to interpret the quality of the work we are doing. Popular Evaluation Metrics for Text Similarity Some example use cases of text similarity include modeling the relevance of a document to a query in a search engine and understanding similar queries in various AI systems in order to provide uniform responses to users. This is a common, yet tricky, problem within the Natural Language Processing (NLP) domain. Many of the traditional techniques tend to focus on lexical text similarity and they are often much faster to implement than the new deep learning techniques that have slowly risen to stardom.Įssentially, we may define text similarity as attempting to determine how “close” 2 documents are in lexical similarity and semantic similarity. Lexical text similarity aims to identify how similar documents are on a word level.
On the other hand, we have another phenomenon called lexical text similarity. This is quite a difficult problem because of the complexities that come with natural language. This phenomenon describes what we’d refer to as semantic text similarity, where we aim to identify how similar documents are based on the context of each document. Nonetheless, we’d still expect a similarity algorithm to return a score that informs us that the sentences are very similar. Photo by Tim J on Unsplash What Is Text Similarity?Ī human could easily determine that these 2 sentences convey a very similar meaning despite being written in 2 completely different formats The intersection of the 2 sentences only has one word in common, “is”, and it doesn’t provide any insight into how similar the sentences.
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