LSA stands for Latent Semantic Analysis, a natural language processing technique which involves analysing the relationships between documents and terms they contain by producing a set of related concepts.

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LSA Clusters Have the Same Words

I use LSA to create 5 document clusters and identify top 10 words nearest to cluster centroid. I found 4 words appear in all clusters(good, stay, staff, clean) . How to deal with this outcome? Should ...
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How Many Documents are Required for Latent Semantic Indexing?

How many documents are generally required to produce good results for latent semantic indexing? By good results I mean relevant results are given for queries
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How to choose an optimal dimension reduction factor in LSA processing

I'm performing a K-Means clustering on a 400.000 text dataset. After eliminating useless chars and removing stopwords, I get a dictionnary size of around 42000 words. So before doing the clustering, ...
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31 views

Unsupervised Aspect based opinion mining [closed]

I have been working on a project to create a model for Unsupervised Aspect based opinion mining on online reviews ( broadly domain independent). i want to automatically extract features of the product,...
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22 views

Supervised semantic analysis

Dimensional reduction and semantic vectorization techniques like LSA, pLSA, LDA and Random Indexing do not take advantage of semantic labeled data like Explicit Semantic Analysis (ESA). I am looking ...
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34 views

Latent Semantic Analysis: stop words and link words

On many tutorials about how to implement LSA, I see that stop words such as "and" are removed. I understand that we might find them in almost all kind of texts, but the repetition of link words in a ...
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23 views

Question about word/discourse similarity analysis

Question about a investigative journalistic proof involving LDA and prior along with general probability distribution model for an unusual type of problem. Basically I have an email I wrote to a ...
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64 views

How do you compare words or documents using LSA (latent semantic analysis)

As the title says, I am a bit cofunsed in how documents or words are compared using LSA (when I say compare, I am referring to calculate similarities, for instance, cosine similarity). In An ...
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8 views

Semantic space coordinates

I am doing LSA and have questions about the coordinates of semantic space, $A=U*S*V^{t}$. In one literature I found that coordinated are just first k columns or rows of left and right singular ...
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22 views

Extact keywords from texts in a corpus for association rule mining

This is my setting, I want to do association rules mining from a corpus. I want to find rules of the form: If A THEN B Where A and B are words. I am using the APRIORI algorithm for computing the ...
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37 views

LSA projections of documents and terms

I am trying to understand how Latent Semantic Analysis works, reading demonstrations based on singular value decomposition. Let's denote $X$ a $D \times W$ document-term matrix. The $D$ rows of $X$ ...
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47 views

Interpretation of LSI/LSA when reducing the number of documents

Usually LSI/LSA is done on a TermFrequency matrix (each row a document, each column a term) to reduce the dimensionality along the terms dimension (i.e. there are too many words). In that way we would ...
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18 views

Extracting document's keyword after dimensionality reduction

Let's say I have a word document matrix and I applied SVD to this matrix (LSA), and now I have the representation of this matrix in a reduced dimensional space. How could I use this for extracting ...
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43 views

LDA or pLSA for short documents?

I'd like to classify short documents, from a predefined set of words. What algorithm would you suggest, LDA or pLSA ? My use case I have a list of users, and for each user a list of the pages she ...
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7 views

IN PLSA how can we ensure that with k topics we won't get some duplicate distributions

I mean, if we initialize our topic coverage coefficients for document pi_d and word probabilities for each topic P_w with random ...
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2answers
515 views

Latent Dirichlet Allocation vs. pLSA

In the original LDA paper it is stated that: The parameters for a k-topic pLSI model are k multinomial distributions of size V and M mixtures over the k hidden topics. This gives kV +kM ...
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166 views

Latent Semantic Indexing and Data Centering

In PCA it's common to center the data, i.e. preprocess the data matrix such that the columns have zero mean. PCA can be done via SVD, but in this case the data matrix also has to be mean-centered. If ...
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316 views

Which SVD matrix do we use for cosine similarity

In Latent Semantic Analysis, we get 3 matrices from the singular value decomposition (SVD), but I am confused - which matrix do we use for cosine similarity?
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100 views

Structure of semantic relationships using Latent Semantic Analysis [closed]

I am struggling to answer the below question: How would you describe the structure of semantic relationships among the terms from a document collection using principles of Latest Semantic Analysis? ...
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142 views

Calculation of Cosine Similarity using LSA

I am getting negative cosine similarity value between two documents in Latent Semantic analysis. How should it be treated?
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308 views

How are the clustering algorithms using the concept of Latent Semantic Analysis?

I have come across Latent Semantic Analysis, but I could not understand it. Can Latent Semantic Analysis be used by humans in clustering of the data-sets? For convenience let us consider the ...
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46 views

pLSA using EM not converging

I have found a lot of questions related to EM but nothing specific to my question. I am using the EM algorithm to fit the pLSA model. As far as I can tell (multiple rounds of checking the code) I can ...
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2answers
7k views

K-means on cosine similarities vs. Euclidean distance (LSA)

I am using latent semantic analysis to represent a corpus of documents in lower dimensional space. I want to cluster these documents into two groups using k-means. Several years ago, I did this using ...
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1answer
527 views

Different results for Singular Value Decomposition (SVD) using different tools

I am currently implementing Latent Semantic Analysis in Java using the EJML library for the preliminary Singular Value Decomposition (SVD). I am testing my code against the original term frequency ...
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1answer
104 views

Latent semantic classification

How can I create a training data set for document classification using LSA? I have created a term-to-document matrix and have class labels also. I don't know whether to add these class labels in a ...
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17 views

single categorical DV, single countinuous IV

Background: I have a set of student answers to some questions and also their scores, whether the answer is correct or not (1 - not correct, 2 - somewhat correct, 3 - correct). I also have for each ...
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152 views

How can I interpret the results of LSA?

I implemented LSA on MATLAB. I have a $D\times N$ term-document matrix, where $D$: # of words, $N$: # of docs. I did low-rank approximation using SVD, and got $$X_k = U_k \cdot S_k \cdot V_k' (D=1000,...
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219 views

how to identify salient words after LSA?

I used Latent Semantic Analysis (LSA) to extract latent topics (i.e., the polynomials coefficient1*word1 + coefficient2*word2 + ...) from a certain corpus. I know that the larger the (absolute value ...
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69 views

Application of LSA/LSI; Is it common to include the use of an edit distance?

I have been using Latent Semantic Analysis (LSA) or Latent Semantic Indexing (LSI) to identify whether different email addresses belong to the same individual by matching on names used for each email ...
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617 views

Clustering of documents that are very different in number of words

I have a corpus of 643 documents with different sizes and my goal is to cluster them according their topics and label each cluster with semantic name for its main topic. I have tired different ...
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2k views

Computing document similarity in latent semantic analysis

I have a question regarding Latent Semantic Analysis - after performing SVD decomposition of term-document matrix and choosing some number of dimensions, I get the set of new document vectors. Now, ...
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194 views

Probabilistic latent semantic analysis vs bag of words

I am going through probabilistic latent semantic analysis (pLSA) for image classification. I did some image classification experiments with BOW (bag of words) and pLSA but based on my experiments BOW ...
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100 views

Probabilistic latent semantic analysis - objective function

In pLSA we are trying to maximize a likelihood function. The likelihood function can be given as: $L = \Pi_i^N \Pi_j^M P(d_i,w_j)^{n(d_i,w_j)}$ Where, M is the number of words and N is the number of ...
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140 views

Computing similarity in LSA

I have a question regarding Latent Semantic Analysis (LSA) introduced in http://lsa.colorado.edu/papers/JASIS.lsi.90.pdf‎ . On page 14, schemas for calculating similarities between different types of ...
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5k views

LSA vs. PCA (document clustering)

I'm investigation various techniques used in document clustering and I would like to clear some doubts concerning PCA (principal component analysis) and LSA (latent semantic analysis). First thing - ...
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181 views

Is LSA and topic clustering easier in European languages similar to English?

I was watching a talk on latent semantic analysis and the speaker described experience applying LSA and REALLY messy data. He concluded that it demonstrated the difficulty of disambiguation of meaning,...
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671 views

Using LSA for dimension reduction on test data

I am looking to explore using LSA as a dimension reduction technique for some textual data. To be specific I would like to take a matrix of M documents and N n-grams (ie: variables) and then create a ...
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274 views

pLSA using tempered EM

In an article by Hofmann pdf, he proposes: initialize $β$ to one, run until convergence, then rescale $β$ by a factor $η<1$, run again until convergence, and iterate this until changing $β$...
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119 views

Is that overfitting?

Given a document-term matrix $X$, where $$X(d, t) = \textit{occurrences of 't' in 'd'}$$, it's possible to compute it's Truncated Singular Value Decomposition:$$X_k = U_k \Sigma_k V_k^T$$ Then, for a ...
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909 views

A parellel between LSA and pLSA

In the original paper of pLSA the author, Thomas Hoffman, draw a parallel between pLSA and LSA data structures that I would like to discuss with you. Background: Taking inspiration the Information ...
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218 views

Derivation of M-step for pLSA

I was looking at section 6 of these notes and trying to understand the derivation of the M-step at the top of page 10. I understood the derivation for the model without background, but I do not ...
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132 views

Initialization in pLSA

I'm using pLSA (Probabilistic Latent Semantic Analysis). I'm trying to estimate the best value for some parameters with a process of k-fold cross-validation. I have noticed that the model fitting is ...
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1answer
3k views

How to cluster LDA/LSI topics generated by gensim?

I'm an enthusiastic single developer working on a small start-up idea. I reduced a corpus of mine to an LSA/LDA vector space using gensim. Now I have a bunch of ...
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636 views

Fast alternatives to the EM algorithm

Are there any speedy alternatives to the EM algorithm for learning models with latent variables (especially pLSA)? I'm okay with sacrificing precision in favor of speed.
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88 views

Is there a sequential version of probabilistic latent semantic analysis?

Does someone know if it exists some way to do online learning with pLSA? The model training is really time consuming, so it is not feasible to rebuild it after every changes in the data.
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122 views

Finding similarity between a reference and few working documents

I have to find the similarity between a reference document and a set of documents in a repository . Here is my method : ...
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661 views

Can LSA be used for document similarity?

I have to find the similarity between two documents. The two documents are simple text documents and i have to report a score. I was using cosine similarity initially. But I was told that LSA is a ...
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1answer
3k views

When to choose PCA vs. LSA/LSI

Question: Are there any general guidelines with respect to the input data characteristics, that can be used to decide between applying PCA versus LSA/LSI? Brief summary of PCA vs. LSA/LSI: ...
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1answer
500 views

pLSA - Probabilistic Latent Semantic Analysis, how to choose topic number?

I am learning about pLSA (Probabilistic Latent Semantic Analysis) right now, in the hopes of being able to apply it to biomolecular annotation prediction. I have a very simple question: How do you ...
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604 views

What is a “tempered EM algorithm”?

In the paper of Probabilistic Latent Semantic Analysis by Hofmann, the author fits the model for document $\times$ word matrix through EM Algorithm in section 3. I was able to follow the derivation ...