what is latent dirichlet allocation (lda) course hero

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What is latent Dirichlet allocation (LDA)?

Latent Dirichlet Allocation. Latent Dirichlet allocation is one of the most popular methods for performing topic modeling. Each document consists of various words and each topic can be associated with some words. The aim behind the LDA to find topics that the document belongs to, on the basis of words contains in it.

What is the difference between latent and Dirichlet distribution?

The word ‘Latent’ indicates that the model discovers the ‘yet-to-be-found’ or hidden topics from the documents. ‘Dirichlet’ indicates LDA’s assumption that the distribution of topics in a document and the distribution of words in topics are both Dirichlet distributions. ‘ Allocation’ indicates the distribution of topics in the document.

What are the 4 terms of the Dirichlet distribution?

On the right side of the equation, there are 4 probability terms, the first two terms represent Dirichlet distribution and the other two represent the multinomial distribution. The first and third terms represent the distribution of topics but the second and fourth represent the word distribution. We will discuss the Dirichlet distribution first.

What is Latent Dirichlet allocation model?

In natural language processing, the latent Dirichlet allocation (LDA) is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.

What does Latent Dirichlet Allocation LDA achieve?

It can help with the following: discovering the hidden themes in the collection. classifying the documents into the discovered themes. using the classification to organize/summarize/search the documents.

What is LDA and how does it work?

LDA is a generative probability model, which means it attempts to provide a model for the distribution of outputs and inputs based on latent variables. This is opposed to discriminative models, which attempt to learn how inputs map to outputs.

What is LDA model in machine learning?

Linear Discriminant Analysis or LDA is a dimensionality reduction technique. It is used as a pre-processing step in Machine Learning and applications of pattern classification.

Is LDA Latent Dirichlet Allocation a supervised machine learning method?

LDA is unsupervised by nature, hence it does not need predefined dictionaries. This means it finds topics automatically, but you cannot control the kind of topics it finds. That's right that LDA is an unsupervised method. However, it could be extended to a supervised one.

What does LDA do in NLP?

LDA is used to classify text in a document to a particular topic. It builds a topic per document model and words per topic model, modeled as Dirichlet distributions.

Why do we use latent Dirichlet allocation?

LDA is a powerful method that allows to identify topics within the documents and map documents to those topics. LDA has many uses to it such as recommending books to customers.

What does LDA stand for?

LDAAcronymDefinitionLDALocal Delivery AgentLDALyme Disease AssociationLDALegal Drinking AgeLDALocal Development Agency (various locations)76 more rows

Is Latent Dirichlet Allocation supervised or unsupervised?

Most topic models, such as latent Dirichlet allocation (LDA) [4], are unsupervised: only the words in the documents are modelled. The goal is to infer topics that maximize the likelihood (or the pos- terior probability) of the collection.

What is the difference between LDA and logistic regression?

LDA works when all the independent/predictor variables are continuous (not categorical) and follow a Normal distribution. Whereas in Logistic Regression this is not the case and categorical variables can be used as independent variables while making predictions.

How does LDA work?

LDA assumes that each document is generated by a statistical generative process. That is, each document is a mix of topics, and each topic is a mix of words. For example, figure 3 shows a document with ten different words. This document could be assumed to be a mix of three topics; tourism, facilities and feedback. Each of these topics, in turn, is a mix of different collections of words. In the process of generating this document, first, a topic is selected from the document-topic distribution and later, from the selected topic, a word is selected from the multinomial topic-word distributions.

What is LDA used for?

LDA has been conventionally used to find thematic word clusters or topics from in text data. Besides this, LDA has also been used as components in more sophisticated applications. Some of the applications are shown below.

What does "latent" mean in a Dirichlet distribution?

The word ‘Latent’ indicates that the model discovers the ‘yet-to-be-found’ or hidden topics from the documents. ‘Dirichlet’ indicates LDA’s assumption that the distribution of topics in a document and the distribution of words in topics are both Dirichlet distributions. ‘ Allocation’ indicates the distribution of topics in the document.

What is topic coherence score?

Topic coherence score is a measure of how good a topic model is in generating coherent topics. A coherent topic should be semantically interpretable and not an artifact of statistical inference. A higher coherence score indicates a better topic model.

How many hyper parameters does LDA have?

LDA has three hyper parameters;#N#1) document-topic density factor ‘α’, shown in step-7 of figure 5,#N#2) topic-word density factor ‘β’, shown in step-8 of figure-5 and#N#3) the number of topics ‘K’ to be considered.

What is the difference between the distributional and mixture assumption?

The distributional assumption indicates that similar topics make use of similar words, and the statistical mixture assumption indicates that each document deals with several topics. Simply put, for a given corpus of documents, each document can be represented as a statistical distribution of a fixed set of topics.

What is the presumption of a law firm?

The presumption is that the documents are not already classified by the smaller law firm. An intuitive way of identifying the documents in such situations is to look for specific sets of keywords and based on the sets of keywords found, identify the type of the documents.

Topic Modeling

Topic modeling is a way of abstract modeling to discover the abstract ‘topics’ that occur in the collections of documents. The idea is that we will perform unsupervised classification on different documents, which find some natural groups in topics. We can answer the following question using topic modeling.

Setting up Generative Model

Let’s suppose we have D documents using the vocabulary of V-word types. Each document consists of an N-words token (can be removed or padded ). Now, we assume K topics, this required a K-dimensional vector that represents the topic distribution for the document.

Graphical Model of LDA

In the above equation, the LHS represents the probability of generating the original document from the LDA machine.

Inference

The inference problem in LDA to compute the posterior of the hidden variables given the document and corpus parameter \alpha and \beta. That is to compute the P (

Example

Let’s consider we have two categories of topics, we have a word vector for each topic consisting of some words. Following are the words that represented different topics: