Topic Models
141 papers with code • 3 benchmarks • 5 datasets
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 the discovery of hidden semantic structures in a text body.
Benchmarks
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arXiv
OpoSum
Greatest papers with code
Software Framework for Topic Modelling with Large Corpora
Large corpora are ubiquitous in today’s world and memory quickly becomes the limiting factor in practical applications of the Vector Space Model (VSM).
Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec
Distributed dense word vectors have been shown to be effective at capturing token-level semantic and syntactic regularities in language, while topic models can form interpretable representations over documents.
Familia: A Configurable Topic Modeling Framework for Industrial Text Engineering
baidu/Familia
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In order to relieve burdens of software engineers without knowledge of Bayesian networks, Familia is able to conduct automatic parameter inference for a variety of topic models.
Familia: An Open-Source Toolkit for Industrial Topic Modeling
baidu/Familia
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Familia is an open-source toolkit for pragmatic topic modeling in industry.
Top2Vec: Distributed Representations of Topics
Distributed representations of documents and words have gained popularity due to their ability to capture semantics of words and documents.
Neural Variational Inference for Text Processing
carpedm20/variational-text-tensorflow
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We validate this framework on two very different text modelling applications, generative document modelling and supervised question answering.
Ranked #1 on
Question Answering
on QASent
Cross-lingual Contextualized Topic Models with Zero-shot Learning
MilaNLProc/contextualized-topic-models
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EACL 2021
They all cover the same content, but the linguistic differences make it impossible to use traditional, bag-of-word-based topic models.
Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence
MilaNLProc/contextualized-topic-models
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Topic models extract groups of words from documents, whose interpretation as a topic hopefully allows for a better understanding of the data.
Topic Modeling in Embedding Spaces
To this end, we develop the Embedded Topic Model (ETM), a generative model of documents that marries traditional topic models with word embeddings.
An Unsupervised Neural Attention Model for Aspect Extraction
Unlike topic models which typically assume independently generated words, word embedding models encourage words that appear in similar contexts to be located close to each other in the embedding space.
