Journal Article10.1007/S10994-013-5413-0
Interactive Topic Modeling
Yuening Hu,Jordan Boyd-Graber,Brianna Satinoff +2 more
- 19 Jun 2011
- Vol. 95, Iss: 3, pp 248-257
TL;DR: This paper presents a mechanism for giving users a voice by encoding users’ feedback to topic models as correlations between words into a topic model, and develops more efficient inference algorithms for tree-based topic models.
read more
Abstract: Topic models have been used extensively as a tool for corpus exploration, and a cottage industry has developed to tweak topic models to better encode human intuitions or to better model data. However, creating such extensions requires expertise in machine learning unavailable to potential end-users of topic modeling software. In this work, we develop a framework for allowing users to iteratively refine the topics discovered by models such as latent Dirichlet allocation (LDA) by adding constraints that enforce that sets of words must appear together in the same topic. We incorporate these constraints interactively by selectively removing elements in the state of a Markov Chain used for inference; we investigate a variety of methods for incorporating this information and demonstrate that these interactively added constraints improve topic usefulness for simulated and actual user sessions.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Aspect extraction for opinion mining with a deep convolutional neural network
TL;DR: This paper used a 7-layer deep convolutional neural network to tag each word in opinionated sentences as either aspect or non-aspect word, and developed a set of linguistic patterns for the same purpose and combined them with the neural network.
916
Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda
Ashraf Abdul,Jo Vermeulen,Danding Wang,Brian Y. Lim,Mohan S. Kankanhalli +4 more
- 21 Apr 2018
TL;DR: This work investigates how HCI researchers can help to develop accountable systems by performing a literature analysis of 289 core papers on explanations and explaina-ble systems, as well as 12,412 citing papers.
814
•Book
Lifelong Machine Learning
Zhiyuan Chen,Bing Liu +1 more
- 07 Nov 2016
TL;DR: As statistical machine learning matures, it is time to make a major effort to break the isolated learning tradition and to study lifelong learning to bring machine learning to new heights.
697
A model of text for experimentation in the social sciences
TL;DR: A hierarchical mixed membership model for analyzing topical content of documents, in which mixing weights are parameterized by observed covariates is posit, enabling researchers to introduce elements of the experimental design that informed document collection into the model, within a generally applicable framework.
652
•Posted Content
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems
TL;DR: A framework with step-by-step design guidelines paired with evaluation methods to close the iterative design and evaluation cycles in multidisciplinary XAI teams is developed and summarized ready-to-use tables of evaluation methods and recommendations for different goals in XAI research are provided.
485
References
Latent dirichlet allocation
TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
•Proceedings Article
Latent Dirichlet Allocation
David M. Blei,Andrew Y. Ng,Michael I. Jordan +2 more
- 03 Jan 2001
TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).
The WEKA data mining software: an update
TL;DR: This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.
•Book
Graph theory
Frank Harary
- 01 Jan 1969
TL;DR: This project focuses on Tutte’s work in cryptography, which enabled the British to read high-level German army messages and has been described as one of the greatest intellectual feats of the war.
18K
Advances in prospect theory: cumulative representation of uncertainty
Amos Tversky,Daniel Kahneman +1 more
TL;DR: Cumulative prospect theory as discussed by the authors applies to uncertain as well as to risky prospects with any number of outcomes, and it allows different weighting functions for gains and for losses, and two principles, diminishing sensitivity and loss aversion, are invoked to explain the characteristic curvature of the value function and the weighting function.
16.1K