LDA Insights | Sumble

What is LDA?

Latent Dirichlet Allocation (LDA) is a generative probabilistic model for collections of discrete data such as text corpora. In the context of text, each document is modeled as a mixture of topics, and each topic is a distribution over words. LDA is commonly used for topic modeling, document classification, and information retrieval to discover underlying thematic structures within a large collection of documents.

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What other technologies are related to LDA?

LDA Competitor Technologies

LSI

Latent Semantic Indexing (LSI) is another topic modeling technique that is used to discover latent relationships between words and documents, serving as a competitor to LDA.

mentioned alongside LDA in 12% (105) of relevant job posts

ELMo

ELMo is a deep learning model that generates word embeddings. While it can be used as an input for LDA, it is also used for generating contextualized word vectors and semantic representations for text analysis, competing with LDA.

mentioned alongside LDA in 11% (71) of relevant job posts

BERT

BERT and similar transformer models can be used for topic extraction and text understanding in ways that compete with LDA.

mentioned alongside LDA in 1% (143) of relevant job posts

LSTM

LSTMs, while not directly used for topic modeling, can be used for tasks like document classification that compete with the purpose of LDA

mentioned alongside LDA in 2% (66) of relevant job posts

GPT

GPT models can be used for various language tasks including topic extraction and text understanding. In some circumstances, GPT can perform topic analysis, competing with LDA

mentioned alongside LDA in 1% (75) of relevant job posts

LDA Complementary Technologies

entity identification and tagging

Entity identification and tagging is a task that is used in conjunction with topic modeling to improve the quality of the topics. Therefore it is complementary to LDA.

mentioned alongside LDA in 44% (66) of relevant job posts

Mahout

Mahout is a scalable machine learning library that includes LDA implementations. It provides tools to use LDA in a distributed environment, so it is complementary.

mentioned alongside LDA in 14% (120) of relevant job posts

Stanford NLP

Stanford NLP provides a suite of NLP tools, including tokenizers, part-of-speech taggers, and named entity recognizers, which are useful for pre-processing text data for LDA. It complements LDA.

mentioned alongside LDA in 30% (56) of relevant job posts