Incorporating Lexical Priors into Topic Models

By Jagadeesh Jagarlamudi, Hal Daumé Iii, Raghavendra Udupa. Conference of the European Chapter of the Association for Computational Linguistics, 2012

341 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/84b81ca748690b830c36fbe74e2ef24829913d9a

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

1. Bing Liu
2. Soujanya Poria
3. Erik Cambria
4. Shuai Wang
5. B. Liu
6. Zixuan Ke
7. Hu Xu
8. Lei Shu

Citations in the corpus

• Interpretable Machine Learning : Lessons from Topic Modeling (2016, background): “Work has been done to design topic models that can incorporate human preferences, such as specifying which words should or should not appear in the same topic [2], or using seed words to guide topics [12] [18].”
• A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries (2018, methodology): “01 (Jagarlamudi et al., 2012), and run 8,000 MCMC iterations using the first 4,000 as burn-in.”
• Mining Aspect-Specific Opinion using a Holistic Lifelong Topic Model (2016, methodology): “Since our LAST model can exploit prior aspect and opinion knowledge, it is thus related to knowledge-based topic models such as [2, 18, 41, 33, 17].”
• Large-Scale Cross-Category Analysis of Consumer Review Content on Sales Conversion Leveraging Deep Learning (2019, unknown): “In Model 5, we replace the unsupervised LDA model with the supervised seeded LDA model (Jagarlamudi et al. 2012).”
• Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge (2016, methodology): “To do so, we follow the approach used by Jagarlamudi et al. (2012) to automatically generate anchor words: for each label in a data set, we find the words that have the highest mutual information with the label.”
• Aspect extraction for opinion mining with a deep convolutional neural network (2016, methodology): “In the category of supervised methods, (Jagarlamudi et al., 2012) employed seed words to guide topic models to learn topics of specific interest to a user, while (Wang et al., 2010) and (Mukherjee and Liu, 2012) employed seeding words to extract related product aspects from product reviews.”
• Topic modelling for qualitative studies (2017, methodology): “The LDA modification in Jagarlamudi et al. [36] assumes that documents are generated from a mixture of regular topics and seed topics that produce only seed words, which are also subsequently trained.”
• Mining topics in documents: standing on the shoulders of big data (2014, background): “, [1, 2, 9, 10, 14, 15, 26, 28]) can exploit such prior domain knowledge to produce better topics.”