Topic model | EPFL Graph Search

Are you an EPFL student looking for a semester project?

Work with us on data science and visualisation projects, and deploy your project as an app on top of GraphSearch.

In statistics and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. Intuitively, given that a document is about a particular topic, one would expect particular words to appear in the document more or less frequently: "dog" and "bone" will appear more often in documents about dogs, "cat" and "meow" will appear in documents about cats, and "the" and "is" will appear approximately equally in both. A document typically concerns multiple topics in different proportions; thus, in a document that is 10% about cats and 90% about dogs, there would probably be about 9 times more dog words than cat words. The "topics" produced by topic modeling techniques are clusters of similar words. A topic model captures this intuition in a mathematical framework, which allows examining a set of documents and discovering, based on the statistics of the words in each, what the topics might be and what each document's balance of topics is. Topic models are also referred to as probabilistic topic models, which refers to statistical algorithms for discovering the latent semantic structures of an extensive text body. In the age of information, the amount of the written material we encounter each day is simply beyond our processing capacity. Topic models can help to organize and offer insights for us to understand large collections of unstructured text bodies. Originally developed as a text-mining tool, topic models have been used to detect instructive structures in data such as genetic information, images, and networks. They also have applications in other fields such as bioinformatics and computer vision. An early topic model was described by Papadimitriou, Raghavan, Tamaki and Vempala in 1998. Another one, called probabilistic latent semantic analysis (PLSA), was created by Thomas Hofmann in 1999.

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications (26)

Related people (11)

Related units (1)

Related concepts (8)

Related courses (8)

Related lectures (46)

Related MOOCs (4)

EE-724: Human language technology: applications to information access

The Human Language Technology (HLT) course introduces methods and applications for language processing and generation, using statistical learning and neural networks.

DH-406: Machine learning for DH

This course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and imple

CS-401: Applied data analysis

This course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the dat

In natural language processing, Latent Dirichlet Allocation (LDA) is a Bayesian network (and, therefore, a generative statistical model) that explains a set of observations through unobserved groups, and each group explains why some parts of the data are similar. The LDA is an example of a Bayesian topic model. In this, observations (e.g., words) are collected into documents, and each word's presence is attributable to one of the document's topics. Each document will contain a small number of topics.

Probabilistic latent semantic analysis (PLSA), also known as probabilistic latent semantic indexing (PLSI, especially in information retrieval circles) is a statistical technique for the analysis of two-mode and co-occurrence data. In effect, one can derive a low-dimensional representation of the observed variables in terms of their affinity to certain hidden variables, just as in latent semantic analysis, from which PLSA evolved.

Technology Strategy in Dynamic Environments: A Computational Analysis of the Automation of Routines, the Organization of AI, and the Evaluation of Technology Risk

In this dissertation, I develop theory and evidence to argue that new technologies are central to how firms organize to create and capture value. I use computational methods such as reinforcement lear

EPFL2022

Natural Language Processing (NLP) driven categorisation and detection of discourse in historical US patents

Jérôme Baudry, Nicolas Christophe Chachereau, Prakhar Gupta, Bhargav Srinivasa Desikan

Patents have traditionally been used in the history of technology as an indication of the thinking process of the inventors, of the challenges or “reverse salients” they faced, or of the social groups

2022

Fitting summary statistics of neural data with a differentiable spiking network simulator

Wulfram Gerstner, Johanni Michael Brea, Alireza Modirshanechi, Shuqi Wang

Fitting network models to neural activity is an important tool in neuroscience. A popular approach is to model a brain area with a probabilistic recurrent spiking network whose parameters maximize the

2021

The activity of neurons in the brain and the code used by these neurons is described by mathematical neuron models at different levels of detail.

This course explains the mathematical and computational models that are used in the field of theoretical neuroscience to analyze the collective dynamics of thousands of interacting neurons.

Introduces Latent Dirichlet Allocation for topic modeling in documents, discussing its process, applications, and limitations.

Introduces topic models, covering clustering, GMM, LDA, Dirichlet distribution, and variational inference.

Explores topic models, Gaussian mixture models, Latent Dirichlet Allocation, and variational inference in understanding latent structures within data.