Questions tagged [vector-space-models]

The tag has no usage guidance.

Filter by
Sorted by
Tagged with
0
votes
0answers
4 views

Difference in Plots of VAR Plot_Forecast vs Simple Overlay on Pyplot

I am sure, I am doing some thing extremely silly but for the life of me not able to figure our cause of the issue. So here it is: Step 0: Form my training set ...
0
votes
1answer
12 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 ...
1
vote
1answer
10 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 ...
0
votes
1answer
23 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-...
0
votes
0answers
70 views

Building Search Engine using Vector Space Model using a private database

Im trying to build a search engine for a private dataset using vector space model and have encountered following problem. Dataset Dataset is private. It is a collection of unstructed pdf . I have ...
1
vote
0answers
2k 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: ...
1
vote
2answers
317 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 ...
2
votes
1answer
79 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?
0
votes
1answer
576 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 ...
0
votes
0answers
13 views

How to include engineered features as factors, along with a CountVectorizer matrix?

I'm doing a classification task on Excel files. For the example in this picture, it would not really make sense to include the 4 columns on the right inside the CountVectorizer matrix. From my ...
0
votes
0answers
12 views

How to vectorize unigrams character to use LSH functions?

I would like to implement fuzzy search based on Bloom Filter and LSH hashing. The problem is that: I have found almost ready package to get ngrams from words, now I don't know how to generate vector ...
1
vote
0answers
25 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 ?
2
votes
2answers
86 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 ...
1
vote
3answers
596 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, ...
6
votes
2answers
3k 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 ...
1
vote
0answers
23 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 ...
1
vote
1answer
44 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 ...
3
votes
2answers
105 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 ...
1
vote
0answers
47 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 ...
2
votes
2answers
566 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 ...
-1
votes
1answer
424 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 ...