![]() ![]() Installing Python3.7 and its virtual environment toolįirst, install Python3. The code is based on what can be found in the To run the evaluation code, contained in the Jupyter Notebook file evaluation.ipynb, you can follow the following steps:Įvaluation 1. In this context, semantic similarity is a technique that assigns a numeric value to a pair of concepts based on the similarity of their meaning, extracted from. Semantic Similarity is the task of determining how similar two sentences are, in terms of what they mean. Load.py: contains a set of functions to load and preprocess the different data sets used. word2vec/get_word2vec_embeddings.bash: script that downloads the Word2Vec word embeddings set used.Įvaluation.ipynb: Jupyter Notebook file in which the evaluation carried out is developed.get_glove_embeddings.bash: script that downloads the GloVe word embeddings set used.2word2vec.py: transforms the GloVe vector set to Word2Vec format.Use a different model with a distinct attention mechanism, like Longformer. Split article text into several smaller texts, apply BERT on every one of them and them combine all the vectors (how to). fasttext/get_fasttext_embeddings.bash: script that downloads the set of word vectors computed with the FastText used. Short-term memory for word sequences as a function of acoustic, semantic and formal similarity. 1 day ago &0183 &32 So far, I basically found 3 approaches: Truncate article text (only use the beginning of it, for example).get_datasets.bash: script to download the datasets used in the evaluation, which is a modification of the one provided in the SentEval toolkit. ![]() Comparison of methods based on pre-trained Word2Vec, GloVe and FastText vectors to measure the semantic similarity between sentence pairs Content
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