Questions tagged [latent-dirichlet-alloc]

Latent Dirichlet Allocation (LDA) is an unsupervised, statistical approach to document modeling that discovers latent semantic topics in large collections of text.

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What are the current approaches to topic modelling (i.e. better than LSA, LDA, LSI)?

I am looking into methods for topic modeling with the purpose of keyword generation. Given a corpus consisting of multiple documents, I would like to get a list of semantically relevant and ...
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Topic modeling with time data

I want to predict the topic of a project tracker narrative entry to identify stages of the project. This entry is written by employees describing what they did in the project. The projects are ...
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118 views

Latent Dirichlet Allocation and topic distributions

When reading about the LDA, the generative procedure for a document is often presented as follows: For each topic $k\in \{1,\ldots, K\}$ Draw a distribution over words $\phi_k\sim \text{Dirichlet}(\...
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Measuring similarity between two objects across multiple categories

I'm constructing a crude measure to gauge the similarity between any pairs of objects across multiple dimensions (or categories (for example, they can be percentages of GDP across economic sectors or ...
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29 views

Understanding original LDA article

I decided to write a different question as a follow up to a comment here about LDA : Upgrading weight parameters to random variable in Gaussian mixtures I am trying to read about latent dirichlet ...
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Conditional probabilities in Latent Dirichlet Allocation Algorithm

In this description of the LDA algorithm, where $t_k$ is the $k$-th topic, $w_j$ is the $j$-th word and $d_i$ is the $i$-th document, the following steps are given Step 1 : randomly assign topic ...
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Labeling LDA priors

Latent dirichlet allocation suffers from the identifiability problem in part due to the exchangeable priors, which results in $k!$ equivalent clusterings that are just permutations of each other. It ...
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Using Topic Modeling for Text Classification

I'm trying to use topic modeling using Latent Dirichlet Allocation as input for text classification problem. Although, I'm not getting good results by doing this. The data has three variables doc id, ...
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I'm trying to identify the posterior distributions in LDiA. Are theta and phi in this PGM the posterior distributions of LDiA?

My understanding is that alpha and beta are the dirichlet priors, and the posteriors would be theta and phi. And since theta and phi are from the same family of distributions as alpha and beta, the ...
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Latent dirichlet allocation as generative model

If Latent Dirichlet allocation is a generative model, then why python library: sklearn.decomposition.LatentDirichletAllocation dosen't generate any new documents ...
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In the dirichlet distribution, does each x represent a particular distribution on the simplex?

The dirichlet distribution takes this form: I'm trying to understand how we end up with a vector from this formula rather than a scalar. The only way I see how is that each x itself is a vector ...
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In Latent Dirichlet allocation, is the following formula the probability of observing a single document, or an entire corpus?

This is the formula in question: Source: https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation
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Latent Dirichlet Allocation: Smoothing

Regarding the paper on LDA via Variational Inference by Blei, et. al., the authors provide a derivation for the basic model, i.e., the variables $\alpha, \beta,\gamma$ and $\phi$. However, for the ...
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No “elbow” with LDA measures c_v & uMass

I am doing some topic modelling experiments - for learning purposes only - with LDA against just over 50k hotel reviews. I am using the c_v and uMass coherence scores but I see no significant change ...
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Latent Dirichlet Allocation - dimensionality of the Dirichlet prior parameter

I seeking some clarity on the dimensionality of the (hyper)parameter $\eta$ of the "smoothed LDA" model in Section 5.4 of the original paper by Blei, Ng, Jordan (2003), which can be found ...
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online LDA with R (topic modelling)

Topic modelling based on online LDA continously updates the model with new data without having to refit the model using the full dataset, ideally without an a priori known vocabulary. This makes it ...
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Using KL divergence to select topic from LDA

I have used sklearn's sklearn.decomposition.LatentDirichletAllocation module to model 10 topics in a set of documents. I have also used the same on a reference text (1 document) and obtained a 1 topic ...
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Why choose a Dirichlet distribution in Latent Dirichlet Allocation (LDA)?

I am studying Latent Dirichlet Allocation (LDA), but I don't have much knowledge of stochastic processes. From Wikipedia: LDA assumes the following generative process for a corpus $D$ consisting of $...
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Topic Modeling: topic coherence score by each topic

I'm currently using NMF, but I am just curious whether I can have coherence scores for each topic. For instance, i want to see whether topic 5 is trained better than topic 1. By looking at the ...
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Interpretation of token weights in LDA topic model?

I'm using Gensim's LDA model for some topic modeling. Once the model has trained, it provides for each topic a coherence score, as well as a list of tuples of weights and tokens. The latter could for ...
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Determine if resume meets requirements of job description

What would be the best approach to determining if, or how much a resume meets the requirements of a job description. I understand you could extract features from both texts with Latent Dirichlet ...
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1answer
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Suggestions for identifying the most “important” image labels

I have a table with images and their assigned object (i.e. specific sub-parts) and image (i.e. image as a whole) labels. Each image may have multiple object labels but only 2 whole-image labels. I ...
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How to know if LDA(Latent Dirichlet Allocation) is applicable to a particular problem? [closed]

Suppose my database has many tables and each table has many columns. I want to use column names of all tables to figure out "major data contents" in my database. Example columns are ...
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Browser history topic clustering

I have a detailed browser history where each event has a visit time, visit duration, URL, and page title. For example let's consider this browser history (page titles only): is Thailand open for ...
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Gibbs sampler of a generative model

I understand what a Gibbs sampler is and I understand how LDA does classification. But I'm unsure how I can generate a Gibbs sampler for an LDA model and how to meld the two concepts. Let's say I ...
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177 views

How to use LDA to classify documents into pre defined topics

LDA is unsupervised and it classifies documents into topics. But, is there a way to make the LDA classify the documents into the predefined (or specific desired) topics. Below link says we need custom ...
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Why is Latent Dirichlet Allocation based on word sequences and not counts?

I assumed that LDA was a bag-of-words approach and that words could be exchanged within a document. However, reviewing more in-depth the Gibbs sampling equations, I noted that the compound Dirichlet-...
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Balanced corpus in topic models

I am building a topic model for a corpus of webpages extracted from a random subset of domains, the topics seems to be ok, but often I see very similar topics, which makes me think I should reduce the ...
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134 views

Why VAE need reparameterization trick while LDA does not (both using variational inference for optimization)

Both VAE and LDA (latent Dirichlet allocation) is based on variational inference, and they both try to optimize ELBO objective function Variational autoencoders use reparameterization so that "...
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Updating an unsupervised model but retaining similarity

In my example I am using topic modelling (specifically a version of LDA) although I think avenues for exploring this could relate to other unsupervised techniques like clustering. I train a model and ...
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Can you restrict the terms Latent Dirichlet Allocation(LDA) uses in specific topics?

I admit I do not have the full understanding of the inner-working of Latent-Dirichlet Allocation(LDA) yet. I have used the sklearn implementation on my text corpus. ...
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Gensim LDA Topic-Term matrix all Zero

I meet a confusing problem when using gensim.models.ldamodel for topic modeling. I have cleaned my documents set and extract the dictionary as suggested in LDA ...
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Topic modelling when the “documents” are not static and new tokens are generated regularly

This question is related to this one and this one. As background, I'm working with Magic: the Gathering decklist data; I'm trying to automatically classify decks into different archetypes based on the ...
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Specify some words are not important and some words are extra important in topic modelling

I am using Latent Dirichlet Allocation in sklearn to model topics. I have a list of words that are likely to inform topics and a list of words that are not important (like stop words). Is there a way ...
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parameter estimation on the LDA model

I have a problem with estimating the parameters of $\theta$, and $\phi$ in the Latent Dirichlet Allocation (LDA) model. The article Finding scientific topics has done the estimation of the parameters ...
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216 views

Can Latent Dirichlet Allocation (LDA) be used to generate word embeddings?

In the original Word2Vec paper (Efficient Estimation of Word Representations in Vector Space, Mikolov et al. 2013), I came across this phrase: Many different types of models were proposed for ...
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261 views

What does each graph quadrant mean with LDAvis plot in R?

I write about the graph explained in this video that shows a big probability mass. It's a web-based interactive visualization of topics estimated using Latent Dirichlet Allocation that is built using ...
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What does each graph cuadrant means with LDA vis plot from R? [duplicate]

I am speeking about this graph, that shows a big probability mass. This is the way that it finally shows with some data I found online and used to understand Latent Dirichlet Allocation topic ...
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136 views

Inference on Dirichlet hyper-parameter

I'm working on a Gibbs sampler for a (somewhat custom version of) Latent Dirichlet Allocation model. In short, I have data that comes from a $K$-dimensional Dirichlet-Multinomial distribution, i.e. $$...
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69 views

LDA alpha equivalent in structural topic model

I'm using an implementation of the structural topic model (stm), written in R using the stm package. I want to reduce the number of topics that are prevalent in ...
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How to intuitively interpret of topic distributions/coefficients in topic modeling

I know topic modeling such as LDA can provide the distribution of topics in documents, and the distribution of topics represent the important of that topic. However, I'm having a hard time intuitively ...
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318 views

A Bernoulli mixture model with a Dirichlet prior on the parameters

Let's $x_1,...,x_N$ be a set of observation coming from the following generative process: $$ \boldsymbol{\theta} \sim \text{Dirichlet}(\boldsymbol{\alpha})\qquad\boldsymbol{\theta},\boldsymbol\alpha\...
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1answer
95 views

Topic Modeling: How many documents “belong” to a topic?

A frequent question that arises when I present results of topic modeling to novices is: "How many documents belong to topic x?". As topic modeling is probabilistic, I hesitate giving absolute ...
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135 views

Inference of the collapsed Gibbs sampling for LDA

I am trying to understand the inference procedure of collapsed Gibbs sampling for LDA model. I refer to this document and LDA wiki page. I cannot figure out how does it simplify the sample equation ...
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144 views

Custom topic priors in LDA

I've been working with LDA (Latent Dirichlet Allocation topic model) for a while now and I believe I have an intermediate understanding of it. The unsupervised nature of LDA is one of its big ...
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gensim LdaModel - How to reduce the number of words in each topic?

I'm trying to get more sparse topics (Less overlaps between different topics). https://radimrehurek.com/gensim/models/ldamodel.html I know it should be determined by the alpha parameter. I've ...
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how to perform statistical analysis on topics produced by topic models?

I have a large corpus of over 1.2 million text documents. After applying LDA to a small sample of the corpus, I have determined the number of topics be 34 using topic coherence score. Here, some ...
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213 views

using latent dirichlet allocation to reduce the number of dimensions in bag of words model?

Does anyone have experience reducing the dimensions in a traditional bag of words model? For example, if you want to train a decision tree on a large set of reviews, the size of the vocabulary ...
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59 views

Prerequisites for Latent Dirichlet Allocation

I have read several "intuitive" introductions to LDA. However, I now want to learn it properly. I have already read through most of Duda, and that was my introduction to data science. However, I ...
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461 views

LDA detect new emerging topics

Thanks for stopping by. I have a directional question - I've built a Latent Dirichlet Allocation using Gensims Mallet wrapper. I trained the model once on OldDataSet.csv and measured coherence. I have ...