I'm trying to similar research abstracts, so I'm using word embeddings to convert words into 1x768 vectors, so overall turning abstracts into embeddings with shape (#ofwords, 768). Cosine similarity between two abstracts returns a matrix (#ofwords1, #ofwords2), which I then sum up to get an overall score. What I'm wondering is if this summing up of all the values in a cosine similarity matrix is really a good way to determine overall similarity between two different texts? Is there a …

I have a study where i want to find users similar to a set of users (SEED). My data looks like a pivot by customer e.g. sample of SEED looks like (note i drop cust_id): cust_id | spend_food | spend_nike | spend_harrods | 1 | 145 | 45 | 32 | 2 | 85 | 89 | 0 | 4 | 23 | 67 | 1900 | 5 | 84 | 12 | 900 | So to find users similar …

count_matrix = count.fit_transform(off_data3['bag_of_words']) I have count_matrix shape with count_matrix.shape (476147, 482824) cosine_sim = cosine_similarity(count_matrix, count_matrix) I think the matrix size is too big to cause this memory error --------------------------------------------------------------------------- MemoryError Traceback (most recent call last) in ~/venv/lib/python3.6/site-packages/sklearn/metrics/pairwise.py in cosine_similarity(X, Y, dense_output) 1034 1035 K = safe_sparse_dot(X_normalized, Y_normalized.T, -> 1036 dense_output=dense_output) 1037 1038 return K ~/venv/lib/python3.6/site-packages/sklearn/utils/extmath.py in safe_sparse_dot(a, b, dense_output) 135 """ 136 if sparse.issparse(a) or sparse.issparse(b): --> 137 ret = a * b 138 if dense_output and hasattr(ret, "toarray"): 139 …

I'm a machine learning beginner and I tried to use the cosine similarity on fuzzy matching purpose. In the following example I want to compare 'data_dirty' with 'data_clean' : When I have to vectorize my data I do not really understand what is the purpose of fit_transform and WHY 'dirty_idf_matrix' has ONLY transform argument with SAME vectorizer than 'clean_idf_matrix' which has saved the value with fit if I understood well. Col_clean = 'fruits_normalized' Col_dirty = 'fruits' #read table data_dirty={f'{Col_dirty}':['I am …

I would like to have a distance measure that takes into account how spread are vectors in a dataset, to weight the absolute distance from one point to another. The Mahalanobis distance does exactly this, but it is a generalization of Euclidean distance, which is not particularly suitable for high-dimensional spaces (see for instance here). Do you know of any measure that is suitable in high-dimensional spaces while also taking into account the correlation between datapoints? Thank you! :)

If I have 3 embeddings Anchor, Positive, Negative from a Siamese model trained with Euclidean distance as distance metric for triplet loss. During inference can cosine similarity similarity be used? I have noticed if I calculate Euclidean distance with model from A, P, N results seem somewhat consistent with matching images getting smaller distance and non-matching images getting bigger distance in most cases. In case I use cosine similarity on above embeddings I am unable to differentiate as similarity values …

I am trying to understand the Siamese networks . In this vector is calculated for an object (say an image) and a distance metric is applied (say manhatten) on two vectors produced by the neural network(s). The idea was applied mostly to images in the tutorials provided on internet. If I compare it with Gensim semantic similarity, there also we have vectors of two objects (words or sentences) and then do a cosine similarity to calculate the difference. (remember example …

I have clustered vectors by cosine distance using nltk clusterer. If I understand correctly, Y axis for elbow method in euclidian distance would be the sum of every distance (squared) between centroid of the cluster with vectors that belongs to that cluster. My question is: Would it be the same for clusters using cosine distance? EDIT: ok, so i tried sum of squares with cosine distance and it seems, that it's returning the same values... heres my code: EDIT2: My …

I am working on recommendation systems wherein I need to match the similarity of 2 users. Now, I know that I can use Tfidf vectorizer to calculate the the cosine similarity score between them. But, now suppose I have some features where I have different priorities for those features. So, for each feature there will be a different priority and the one with with higher priority will be checked first. So, when I get cosine similarity based on that feature, …

To measure the similarity between two documents, one can use, e.g. TF-IDF/Cosine Similarity. Supposing that after calculating the similarity scores of Doc A against a list of Documents (Doc B, Doc C,...), we got: Document Pair Similarity Score Doc A vs. Doc B 0.45 Doc A vs. Doc C 0.30 Doc A vs. ... ... Of course, Doc B seems to be the closest one, in terms of similarity, for Doc A. But what if Users, as humans, think Doc …

I want to tag a list of texts using predefined keywords ex: keyword1, keyword2, keyword3. I can easily achieve this using one to one mapping (If keywords exist in the text tagged as important). But in this way, I cannot find which texts are more important than others(assuming texts contain more than one keywords are more important). To achieve this one I've decided to train word2vec model and extract vectors then calculate cosine similarity between keywords vector and text vector …

I'm new to this so please let me know if my logic of comparing cosine similarity and k-means is incorrect I got a set of 4 clusters from k-means and now I'm interested in the Cluster No. 1. For this cluster, I take the average of all values for each column and keep it aside. Now, I have a test sample, for which I run k-means prediction and I get output as 1, meaning it belongs to Cluster No. 1 …

I just started using word2vec and have no idea how to create vectors (using word2vec) of two lists, each containing set of words and phrases and then how to calculate the cosine similarity between these 2 lists. For example: list1 =['blogs', 'vmware', 'server', 'virtual', 'oracle update', 'virtualization', 'application','infrastructure', 'management'] list2 = ['microsoft visual studio','desktop virtualization', 'microsoft exchange server','cloud computing','windows server 2008'] Any help would be appreciated.

Please comment on the following NLP Interview Coding Task that I have prepared for the candidates on Data Science NLP position that I am looking for. The goal is to check candidate understanding of the fundamental role of text representations with vectors in NLP, as well as checking candidate coding skills and their ability to optimize computations with vectorization that Numpy provides. In particular I need your opinion on: Is task clear? Is task adequate for coding a rough solution …

Is the following statement true? https://stats.stackexchange.com/q/256778 The value of cosine similarity between two terms itself is not indicator whether they are similar or not. If yes then how is use of clustering algorithms like DBSCAN for word embeddings justified? From what I know DBSCAN algorithm only looks to its immediate neighbour to be included in cluster, but it seems the wrong way since maybe we need to check every word with every other word and take top ranked words.

Cosine similarity vs The Levenshtein distance I wanted to know what is the difference between them and in what situations they work best? As per my understanding: Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. The cosine of 0° is 1, and it is less than 1 for any angle in the interval (0,π] radians. The Levenshtein distance is a string metric for …

I've got a set of approx. 50 000 2-dimensional euclidean vectors which are connected with 20 groups, i.e. each group has approx. 2500 2-dimensional euclidean vectors. My data includes endpoints coordinates, i.e. $x_0, y_0, x_1, y_1$. Now I would like to cluster the vectors within these groups, probably using k-means/k-medoids clutering (or other clustering algorithm with pre-defined no. of clusters). What is also important, my main focus is on vector's direction, length is the minor concern (but at best, still …

The problem is to identify the fake news detection, As this is text classification problem . Constraints are basically that we cannot use traditional machine learning and deep learning approaches. If we move towards Machine learning then we can easily sort out this problem, using Naive Bayes or Logistic Regression etc but we cannot use this. I want to take yours suggestion that using cosine similarity can we done this , Take out the text, apply the feature embedding techniques …

I am wondering how to implement k-means++ with cosine distance, acording to quote below (wikipedia), which says, that distance needs to be squared. But with square is lost direction of distance which in my understanding really matters. cos_dist(x,y) = -1 => (-1)^2 = 1 Choose one center uniformly at random among the data points. For each data point x not chosen yet, compute D(x), the distance between x and the nearest center that has already been chosen. Choose one new …

I have a list of documents and I am looking for a) duplicates; b) documents that are very similar. To do so, I proceed as follows: Embed the documents using paraphrase-xlm-r-multilingual-v1. Calculate the cosine similarity between the vector embeddings (code below). All the cosine similarity values I get are between 0 and 1. Why is that? Shouldn't I also have negative cos similarity values? The sentence embeddings have both positive and negative elements. num_docs = np.array(sentence_embedding).shape[0] cos_sim = np.zeros([num_docs, num_docs]) …