Another type of semantic distance is Euclidean Distance. Cosine distance between vector representations is a type of semantic distance. Vector representations of words can be obtained using common models like Word2Vec or high-end Neural Networks like BERT or GPT-2. For example, vectors representing tree and wood would be closer than vectors for king and queen. Here, words are converted into numerical vectors representing their relative meaning. As such, you need a vocabulary here, on top of which a model is built. Semantic distance: This is a measure of how far apart words are in terms of meaning. But they have no real meaning in language. For example, the strings abcd and abed have MED = 1. The words need not have any meaning for MED to be defined. Minimum Edit Distance: This is the number of changes required to make two words have the same characters. The following function might be useful though, if you have several words and you want to have the most similar one from the list: model_glove.most_similar_to_given("camera", )Ĭosine distance is always defined between two real vectors of same length.Īs for words/sentences/strings, there are two kinds of distances:
model_glove.relative_cosine_similarity("kamra", "cameras") Figuring these out is a separate task from cosine similarity. Pretrained models will have a hard time recognising typos though, because they were probably not in the training data. Model_glove.relative_cosine_similarity("economy", "fart") Model_glove.relative_cosine_similarity("film", "camera") Model_glove.relative_cosine_similarity("politics", "vote") Model_glove = api.load("glove-wiki-gigaword-100")
#Python cosine similarity download#
You can can download a pre-trained model and then get the cosine similarity between their two vectors. I'd recommend using a pre-trained model from Gensim.
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