Latent Dirichlet Allocation

By David M. Blei, Andrew Y. Ng, Michael I. Jordan. 2009

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

Citations in the corpus

Learning Transferable Visual Models From Natural Language Supervision (2021, background): “This is an admittedly extremely broad area and covers most work in the field of distributional semantics including topic models (Blei et al., 2003), word, sentence, and paragraph vectors (Mikolov et al.”
Experience Grounds Language (2020, methodology): “, 1988, 1990; Dumais, 2004) and later in the generative framework of Latent Dirichlet Allocation (Blei et al., 2003).”
Transfer Learning in Natural Language Processing (2019, methodology): “…approaches which aim to model the dataset itself, brieﬂy presenting non-neural approaches (Deer-wester et al., 1990; Brown et al., 1993; Blei et al., 2003) before detailing deep neural network approaches like auto-encoding/skip-thoughts models (Dai and Le, 2015; Kiros et al., 2015;…”
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model (2017, background): “, a topic), which is often safe in language modeling (Blei et al., 2003).”
Improving language models by retrieving from trillions of tokens (2021, methodology): “Foundational work use latent topic modelling approaches like LDA (Blei et al., 2003) to identify relevant neighbours (Wei and Croft, 2006).”
Compressive Transformers for Long-Range Sequence Modelling (2019, methodology): “Alongside quantitative analyses, we build an LDA topic model (Blei et al., 2003) for a qualitative inspection of the text.”
Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization (2018, methodology): “The LDA model (Blei et al., 2003) was trained on XSum documents (training portion).”
Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index (2019, background): “Approximate similarity search Sublinear-time search for the nearest neighbor from a large collection of vectors is a signiﬁcant interest to the information retrieval community (Deer-wester et al., 1990; Blei et al., 2003).”
The NarrativeQA Reading Comprehension Challenge (2017, methodology): “…approaches to chunk selection: we retrieve chunks based on the highest Rouge-L or Bleu-1 scoring span with respect to the question in the story; comparing topic distributions from an LDA model (Blei et al., 2003) between questions and chunks according to their symmetric Kullback–Leibler divergence.”
Self-Supervised Learning of Visual Features through Embedding Images into Text Topic Spaces (2017, positive): “For this we represent textual information at the topic level, by leveraging the hidden semantic structures discovered by the Latent Dirichlet Allocation (LDA) topic modeling framework [3], and use this representation as supervision for visual learning as shown in Figure 1.”
GAN Inversion: A Survey (2021, methodology): “For example, we can formulate GAN inversion as decomposing signals into components (matrix factorization problems) and use non-linear Factor Analysis (FA) [208], Independent Component Analysis (ICA) [209], Latent Dirichlet Allocation (LDA) [210], [211] to decompose the network weight and to find int”
Beyond CCA: Moment Matching for Multi-View Models (2016, neutral): “For example, due to the close relation of DCCA and DICA, the former is closely related to the multi-view topic models (see, e.g., Blei & Jordan, 2003).”
Rethinking LDA: Moment Matching for Discrete ICA (2015, neutral): “, [5] and references therein) by showing a strong relationship between latent Dirichlet allocation (LDA) [1] and ICA [6, 7, 8].”
MERLOT: Multimodal Neural Script Knowledge Models (2021, methodology): “We randomly sampled 55K video transcripts, and ran an LDA topic model [16] implemented in MALLET [79] with 100 topics.”
Learning to Generate Reviews and Discovering Sentiment (2017, background): “Topic modelling can also discover factors within a corpus of text which align to human interpretable concepts such as art or education (Blei et al., 2003).”