Questions tagged [vector-space-models]

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What technique is used to combine multiple vectors (of length n) into a single vector of length k?

I want to use some machine learning/neural network or other mathematical model to get this. The length of resultant vector should be similar to n.
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19 views

Combine multiple vector fields for approximate nearest neighbor search

I have multiple vector fields in one collection. My use-case is to find similar sentences in similar contexts. The sentences and contexts are encoded to float vectors. Therefore, I have one vector for ...
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2answers
16 views

Dimensionality reduction of vectors with null values

I have vectors of same length where each entry can have the value 0, 1 or null. V = {[0,1,1,1,null,0], [null,1,0,null,0,1], ...} How can I perform a dimensionality ...
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25 views

Approximate maximum dot product between a vector and set of vectors using only a single vector representation for the latter

If we have a vector $q$ and a set of vectors $D = \{d_1, d_2, ..., d_l\}$ is there a way to create functions $QF$ and $DF$ such that $QF(q)^TDF(D) \approx \max_i(q^Td_i)$ ? Use case: I want to build ...
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1answer
21 views

Dummy vectors and performance measurement for vector search Face Recognition

I have about thousands of person face (from celebrity dataset LFW), which each person represented by 512 x 1 vector. I stored it on vector DB to build face searching system using embedded feature (...
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1answer
35 views

how to calculate similarity between users based on movie ratings

Hi I am working on a movie recommendation system and I have to find alikeness between the main user and other users. For example, the main user watched 3 specific movies and rated them as 8,5,7. A ...
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0answers
17 views

target encoding and weighting

I am working on a project in which I use data of movies and I represent each movie as a vector of length of 15. So there are 15 features ranging from genre to director. Most of the features are ...
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25 views

one hot encoding and multi hot encoding together

Hi for a project I am representing movies in a vectoral format. One thing I want to ask is can I use both one hot encoding and multi hot encoding together on my representation. There are limited ...
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1answer
32 views

Word2Vec: Identifying many-to-one relationships between words

Standard introductory examples in Word2Vec, like king - queen = man - woman and tokyo - japan = london - uk, involve one-to-one ...
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1answer
44 views

Non-commutative distance formula

I am trying to find a distance formula or a method that can give the non-commutative distance between two points in a feature space. Suppose there are two movies represented in an R^n feature space. ...
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0answers
61 views

Is it acceptable to append information to word embeddings?

Let's say I have my 300 dimensional word embedding trained with Word2Vec and it contains 10,000 word vectors. I have additional data on the 10,000 words in the form of a vector (10,000x1), containing ...
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0answers
21 views

How can I model the autocorrelation of objective variables under the situation where we can't observe any actual objective variable in the test phase

I'm trying to model the relationship between the declared value from a subject and stimulus. For example, modeling a relationship between the subject's happiness and strength of stimulus so that we ...
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1answer
40 views

How come same cluster category be separated?

I have these 200 vectors which were clustered using K-means clustering based on keywords weight similarity that was given by TF-IDF (Term Frequency - Inverse Document Frequency). The vectors were ...
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2answers
38 views

Is it accurate to say that "K-means clustering the vectors based on keywords weight similarity"?

Long story short, I have 200 vectors as a result of TF-IDF (Term Frequency - Inverse Document Frequency) on thousands of keywords in hundreds of vectors. The total number of unique keywords that I got ...
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1answer
59 views

what is the difference between positional vector and attention vector used in transformer model?

what is the difference between positional vector and attention vector used in transformer model ? , i saw a video in youtue and the defintion for positional vector was give as :* "vector that ...
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1answer
217 views

How can we use the cosine similarity formula on document feature vector without a direction?

In mathematics, a vector has both magnitude and direction. In data science, for identifying document similarity we convert the document into a feature vector. Then apply cosine angle formula between ...
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2answers
37 views

How to represent a document in test data with the Document-Term Matrix created from the training set?

I build a classifier of documents using the vector representation of each document in the training set (i.e a row in the Document-Term Matrix). Now I need to test the model on the test data. But how ...
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1answer
35 views

Why is n-grams language independent?

I don't understand how n-grams are language independent. I've read that by using character n-grams of a word than the word itself as dimensions of a vector space model, we can skip the language-...
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1answer
189 views

Can 2 different OOV words get the same vector in FastText?

Since FastText sums up the vectors(order is not considered) of an OOV word's subwords, is it possible for two different OOV words to get the same vector ? If so, then can you give an example?
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0answers
4k views

Getting 'ValueError: setting an array element with a sequence.' when attempting to fit mixed-type data

I have already seen this, this and this question, but none of the suggestions seemed to fix my problem (so I have reverted them). I have the following code: ...
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0answers
27 views

What is the best technique to transform documents into vectors?

What is the best algorithm between doc2vec and Singular Value Decomposition (SVD) to transform a set of 600 documents of around 1000 words each into vectors ?
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2answers
238 views

ways to represent document by its keyword vectors

I have documents, say for example, D1, D2, D3... Dm. Every Di has its individual components or keywords k1, k2, k3,... kn, where ki is an n-dimensional vector. The number of individual components ...
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2answers
524 views

How to dual encode two sentences to show similarity score

I've been trying to grasp the concept of Google's semantic experiences. By using it, I'm planning to implement a semantic query tool. With universal sentence encoder I can first pre-encode all ...
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1answer
43 views

Can I sum up feature vectors of a user‘s collection?

I want to find items that are similar to items users already have in their collection. Every item has attributes, so I created feature vectors where every element of the vector represents an attribute ...
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1answer
73 views

What are the main distribution semantics based algorithms?

I am aware that LSI, RRI and word embeddings are distributional semantics models. However, I am not certain if the below mentioned are also distributional semantic models. Non-Negative Tensor ...
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1answer
54 views

How can I train a model to modify a vector by rewarding the model based on the modified vectors nearest neighbors?

I am experimenting with a document retrieval system in which I have documents represented as vectors. When queries come in, they are turned to vectors by the same method as used for the documents. The ...
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3answers
948 views

Machine learning - Algorithm suggestion for my problem using NLP

I am looking for a machine learning algorithm for my problem. I have a set of sentences like, ...
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2answers
4k views

NN embedding layer

Several neural network libraries such as tensorflow and pytorch offer an Embedding layer. Having implemented word2vec in the past, I understand the reasoning behind wanting a lower dimensional ...
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1answer
710 views

Stacking/Concatenating/Combining two vector space models

I have two vector-space models, with different dimensions. The number of vectors in one model is the same as the number of vectors in the other. I.E: if I have vector representation for a car in one ...
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2answers
107 views

Collection Of Variable Length Sequences and Descriptions: A Search Problem

I have a tough problem and need some advice: Suppose I have a collection of variable length sequences, many of which are unique -- imagine the moves to a chess game, eg d4 Nf6 c4 g6 Nc3 Bg7 ...
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2answers
789 views

Confusion with cosine similarity

In information retrieval when we calculate the cosine similarity between the query features vector and the document features vector we penalize the unseen words in the query. Example if we have two ...
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1answer
471 views

How to improve Vector Space Models with semantic similarity?

I try to construct a classic querying system where I find the most probable candidate text for a query by computing cosine similarities of TFIDF vectors of normalized text of possible answers. This ...