LDA-based document models for ad-hoc retrieval

By Xing Wei, W. Bruce Croft. 2006

1,275 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Information Retrieval & Search, Probabilistic & Bayesian Methods

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Citations in the corpus

Improving language models by retrieving from trillions of tokens (2021, methodology): “, 2003) to identify relevant neighbours (Wei and Croft, 2006).”
A Multi-View Embedding Space for Modeling Internet Images, Tags, and Their Semantics (2012, methodology): “This approach is inspired by clusterbased information retrieval (Wei and Croft 2006) and the”
Learning to Match using Local and Distributed Representations of Text for Web Search (2016, methodology): “In contrast, latent semantic analysis (LSA) [5], probabilistic latent semantic analysis (PLSA) [14] and latent Dirichlet allocation (LDA) [2, 40] learn low-dimensional ∗ The author is a part-time PhD student at UCL.”
Explicit Semantic Ranking for Academic Search via Knowledge Graph Embedding (2017, methodology): “Topic modeling techniques have been used to ﬁrst map query and document into a latent space, and then matching them in it [24].”
Deep Relevance Ranking Using Enhanced Document-Query Interactions (2018, methodology): “With the advent of statistical NLP and statistical IR, probabilistic language and topic modeling were explored (Zhai and Lafferty, 2001; Wei and Croft, 2006), followed recently by deep learning IR”
Spoken Content Retrieval—Beyond Cascading Speech Recognition with Text Retrieval (2015, methodology): “In addition, regularizing the estimation process by the original query language model was shown to yield better retrieval performance, and this approach is known as the query-regularized mixture model [167], [259], [260], [263], [265], [266].”
WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation (2015, background): “Our empirical results in a wide range of testing conditions demonstrate that WarpLDA is consistently 5-15x faster than the state-of-the-art MH-based LightLDA, and is faster than the state-of-the-art sparsity aware F+LDA in most settings.”
Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval (2007, methodology): “Similar to the method in [15], we can combine the query likelihood from the basic language model and the likelihood from the TNG model in various ways.”
Analysis of the Paragraph Vector Model for Information Retrieval (2016, methodology): “As observed by previous studies [24, 15, 1], topic level estimation is beneficial for language modeling approach.”
Monolingual and Cross-Lingual Information Retrieval Models Based on (Bilingual) Word Embeddings (2015, methodology): “The LDA-based MoIR model was introduced in [44], while the description of the BiLDA-based CLIR model is available in [41].”
A location-based news article recommendation with explicit localized semantic analysis (2013, background): “Word frequencies or topics are the most widely used representation of articles [11, 18], but recently, topic representation is preferred because a topic is a good proxy for article contents [23].”
Semantic Matching in Search (2014, background): “Previous work shows that the uses of topic models can indeed improve search relevance [58, 184, 200, 180, 179].”
Word Embedding based Generalized Language Model for Information Retrieval (2015, methodology): “In contrast to [9], which reports retrieval results with LDA smoothed LM (LDA-LM) on individual document subsets (and their corresponding relevance judgements) from the TREC collection as categorized by their sources, i.”
A Topic Model for Word Sense Disambiguation (2007, methodology): “Topic models have recently been applied to information retrieval (Wei and Croft, 2006), text classification (Blei et al.”
Mining Text Data (2012, methodology): “Many have adopted this seminal approach, mostly in its generic version, sometimes using different measures of novelty to select new sentences [91, 101, 65].”