Like the other steps, vectorization is taken care of automatically with the nlp() call. Since you already have a list of token objects, you can get the vector representation of one of the tokens like so: Python >>> filtered_tokens[1].vector array([ 1.8371646 , 1.4529226 , -1.6147211 ...
Vectorization To compute any of the above, the simplest way is to convert everything to a vector and then compute the cosine similarity. So, let’s convert the query and documents to vectors. We are going to use total_vocab variable which has all the list of unique tokens...
Our previously proposed approach utilizing LDA [39] is considered our baseline. We use the scikit-learn LDA model with 90 topics, 100 iterations, and a learning rate decay of 0.5. For preprocessing, we use the WordNetLemmatizer from the nltk framework, and regarding vectorization, we use the...
For preprocessing, we use the WordNetLemmatizer from the nltk framework, and regarding vectorization, we use the CountVectorizer from scikit-learn with a maximal df value of 0.5, a minimum value of 2, and our ngram range is 1.1. Our BERTopic [30] model uses the bge-large-en-v1.5 ...
LPV: A Log Parser Based on Vectorization for Offline and Online Log Parsing IEEE International Conference on Data Mining (ICDM), 2020 (2020), pp. 1346-1351 CrossrefView in ScopusGoogle Scholar [5] R.B. Yadav, P.S. Kumar, S.V. Dhavale A Survey on Log Anomaly Detection using Deep Lea...