About

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.

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Greatest papers with code

Software Framework for Topic Modelling with Large Corpora

Workshop On New Challenges For NLP Frameworks 2010 RaRe-Technologies/gensim

Large corpora are ubiquitous in today’s world and memory quickly becomes the limiting factor in practical applications of the Vector Space Model (VSM).

TOPIC MODELS

Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec

6 May 2016cemoody/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.

TOPIC MODELS WORD EMBEDDINGS

Familia: A Configurable Topic Modeling Framework for Industrial Text Engineering

11 Aug 2018baidu/Familia

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.

TOPIC MODELS

Inferring Networks of Substitutable and Complementary Products

29 Jun 2015snap-stanford/snap

These two types of recommendations are referred to as substitutes and complements: substitutes are products that can be purchased instead of each other, while complements are products that can be purchased in addition to each other.

LINK PREDICTION RECOMMENDATION SYSTEMS TOPIC MODELS

Top2Vec: Distributed Representations of Topics

19 Aug 2020ddangelov/Top2Vec

Distributed representations of documents and words have gained popularity due to their ability to capture semantics of words and documents.

LEMMATIZATION SEMANTIC SIMILARITY SEMANTIC TEXTUAL SIMILARITY TOPIC MODELS

Neural Variational Inference for Text Processing

19 Nov 2015carpedm20/variational-text-tensorflow

We validate this framework on two very different text modelling applications, generative document modelling and supervised question answering.

ANSWER SELECTION LATENT VARIABLE MODELS TOPIC MODELS VARIATIONAL INFERENCE

Cross-lingual Contextualized Topic Models with Zero-shot Learning

16 Apr 2020MilaNLProc/contextualized-topic-models

They all cover the same content, but the linguistic differences make it impossible to use traditional, bag-of-word-based topic models.

TOPIC MODELS TRANSFER LEARNING VARIATIONAL INFERENCE ZERO-SHOT LEARNING

Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence

8 Apr 2020MilaNLProc/contextualized-topic-models

Topic models extract meaningful groups of words from documents, allowing for a better understanding of data.

SENTENCE EMBEDDINGS TOPIC MODELS

Topic Modeling in Embedding Spaces

TACL 2020 adjidieng/ETM

To this end, we develop the Embedded Topic Model (ETM), a generative model of documents that marries traditional topic models with word embeddings.

TOPIC MODELS VARIATIONAL INFERENCE WORD EMBEDDINGS

An Unsupervised Neural Attention Model for Aspect Extraction

ACL 2017 ruidan/Unsupervised-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.

ASPECT EXTRACTION DOMAIN ADAPTATION TOPIC MODELS WORD EMBEDDINGS