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"Topic Modeling": models, code, and papers

Interactions in information spread: quantification and interpretation using stochastic block models

Apr 09, 2020
Gaël Poux-Médard, Julien Velcin, Sabine Loudcher

In most real-world applications, it is seldom the case that a given observable evolves independently of its environment. In social networks, users' behavior results from the people they interact with, news in their feed, or trending topics. In natural language, the meaning of phrases emerges from the combination of words. In general medicine, a diagnosis is established on the basis of the interaction of symptoms. Here, we propose a new model, the Interactive Mixed Membership Stochastic Block Model (IMMSBM), which investigates the role of interactions between entities (hashtags, words, memes, etc.) and quantifies their importance within the aforementioned corpora. We find that interactions play an important role in those corpora. In inference tasks, taking them into account leads to average relative changes with respect to non-interactive models of up to 150\% in the probability of an outcome. Furthermore, their role greatly improves the predictive power of the model. Our findings suggest that neglecting interactions when modeling real-world phenomena might lead to incorrect conclusions being drawn.

* 17 pages, 3 figures, submitted to ECML-PKDD 2020 
  

Topic Detection and Summarization of User Reviews

May 30, 2020
Pengyuan Li, Lei Huang, Guang-jie Ren

A massive amount of reviews are generated daily from various platforms. It is impossible for people to read through tons of reviews and to obtain useful information. Automatic summarizing customer reviews thus is important for identifying and extracting the essential information to help users to obtain the gist of the data. However, as customer reviews are typically short, informal, and multifaceted, it is extremely challenging to generate topic-wise summarization.While there are several studies aims to solve this issue, they are heuristic methods that are developed only utilizing customer reviews. Unlike existing method, we propose an effective new summarization method by analyzing both reviews and summaries.To do that, we first segment reviews and summaries into individual sentiments. As the sentiments are typically short, we combine sentiments talking about the same aspect into a single document and apply topic modeling method to identify hidden topics among customer reviews and summaries. Sentiment analysis is employed to distinguish positive and negative opinions among each detected topic. A classifier is also introduced to distinguish the writing pattern of summaries and that of customer reviews. Finally, sentiments are selected to generate the summarization based on their topic relevance, sentiment analysis score and the writing pattern. To test our method, a new dataset comprising product reviews and summaries about 1028 products are collected from Amazon and CNET. Experimental results show the effectiveness of our method compared with other methods.

  

Improved Patient Classification with Language Model Pretraining Over Clinical Notes

Sep 06, 2019
Jonas Kemp, Alvin Rajkomar, Andrew M. Dai

Clinical notes in electronic health records contain highly heterogeneous writing styles, including non-standard terminology or abbreviations. Using these notes in predictive modeling has traditionally required preprocessing (e.g. taking frequent terms or topic modeling) that removes much of the richness of the source data. We propose a pretrained hierarchical recurrent neural network model that parses minimally processed clinical notes in an intuitive fashion, and show that it improves performance for multiple classification tasks on the Medical Information Mart for Intensive Care III (MIMIC-III) dataset, increasing top-5 recall to 89.7% (up by 4.8%) for primary diagnosis classification and AUPRC to 35.2% (up by 2.4%) for multilabel diagnosis classification compared to models that treat the notes as an unordered collection of terms or without pretraining. We also apply an attribution technique to several examples to identify the words and the nearby context that the model uses to make its prediction, and show the importance of the words' context.

  

Non-Pharmaceutical Intervention Discovery with Topic Modeling

Sep 10, 2020
Jonathan Smith, Borna Ghotbi, Seungeun Yi, Mahboobeh Parsapoor

We consider the task of discovering categories of non-pharmaceutical interventions during the evolving COVID-19 pandemic. We explore topic modeling on two corpora with national and international scope. These models discover existing categories when compared with human intervention labels while reduced human effort needed.

* ML for Global Health (ICML 2020 Workshop) 
  

Neural Models for Documents with Metadata

Oct 23, 2018
Dallas Card, Chenhao Tan, Noah A. Smith

Most real-world document collections involve various types of metadata, such as author, source, and date, and yet the most commonly-used approaches to modeling text corpora ignore this information. While specialized models have been developed for particular applications, few are widely used in practice, as customization typically requires derivation of a custom inference algorithm. In this paper, we build on recent advances in variational inference methods and propose a general neural framework, based on topic models, to enable flexible incorporation of metadata and allow for rapid exploration of alternative models. Our approach achieves strong performance, with a manageable tradeoff between perplexity, coherence, and sparsity. Finally, we demonstrate the potential of our framework through an exploration of a corpus of articles about US immigration.

* Dallas Card, Chenhao Tan, and Noah A. Smith. (2018). Neural Models for Documents with Metadata. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 
* 13 pages, 3 figures, 6 tables; updating to version published at ACL 2018 
  

Inductive Granger Causal Modeling for Multivariate Time Series

Feb 10, 2021
Yunfei Chu, Xiaowei Wang, Jianxin Ma, Kunyang Jia, Jingren Zhou, Hongxia Yang

Granger causal modeling is an emerging topic that can uncover Granger causal relationship behind multivariate time series data. In many real-world systems, it is common to encounter a large amount of multivariate time series data collected from different individuals with sharing commonalities. However, there are ongoing concerns regarding Granger causality's applicability in such large scale complex scenarios, presenting both challenges and opportunities for Granger causal structure reconstruction. Existing methods usually train a distinct model for each individual, suffering from inefficiency and over-fitting issues. To bridge this gap, we propose an Inductive GRanger cAusal modeling (InGRA) framework for inductive Granger causality learning and common causal structure detection on multivariate time series, which exploits the shared commonalities underlying the different individuals. In particular, we train one global model for individuals with different Granger causal structures through a novel attention mechanism, called prototypical Granger causal attention. The model can detect common causal structures for different individuals and infer Granger causal structures for newly arrived individuals. Extensive experiments, as well as an online A/B test on an E-commercial advertising platform, demonstrate the superior performances of InGRA.

* 6 pages, 6 figures 
  

Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting

Apr 05, 2020
Qi Wang, Tao Han, Junyu Gao, Yuan Yuan

Cross-domain crowd counting (CDCC) is a hot topic due to its importance in public safety. The purpose of CDCC is to reduce the domain shift between the source and target domain. Recently, typical methods attempt to extract domain-invariant features via image translation and adversarial learning. When it comes to specific tasks, we find that the final manifestation of the task gap is in the parameters of the model, and the domain shift can be represented apparently by the differences in model weights. To describe the domain gap directly at the parameter-level, we propose a Neuron Linear Transformation (NLT) method, where NLT is exploited to learn the shift at neuron-level and then transfer the source model to the target model. Specifically, for a specific neuron of a source model, NLT exploits few labeled target data to learn a group of parameters, which updates the target neuron via a linear transformation. Extensive experiments and analysis on six real-world datasets validate that NLT achieves top performance compared with other domain adaptation methods. An ablation study also shows that the NLT is robust and more effective compare with supervised and fine-tune training. Furthermore, we will release the code after the paper is accepted.

* 12 paegs, 8 figures 
  

Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction

Apr 18, 2022
Likang Wu, Hao Wang, Enhong Chen, Zhi Li, Hongke Zhao, Jianhui Ma

Network-aware cascade size prediction aims to predict the final reposted number of user-generated information via modeling the propagation process in social networks. Estimating the user's reposting probability by social influence, namely state activation plays an important role in the information diffusion process. Therefore, Graph Neural Networks (GNN), which can simulate the information interaction between nodes, has been proved as an effective scheme to handle this prediction task. However, existing studies including GNN-based models usually neglect a vital factor of user's preference which influences the state activation deeply. To that end, we propose a novel framework to promote cascade size prediction by enhancing the user preference modeling according to three stages, i.e., preference topics generation, preference shift modeling, and social influence activation. Our end-to-end method makes the user activating process of information diffusion more adaptive and accurate. Extensive experiments on two large-scale real-world datasets have clearly demonstrated the effectiveness of our proposed model compared to state-of-the-art baselines.

* SIGIR 2022 
  

Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing

Jun 11, 2022
Jie Chen, Min Zhao, Xiuheng Wang, Cédric Richard, Susanto Rahardja

Spectral unmixing is one of the most important quantitative analysis tasks in hyperspectral data processing. Conventional physics-based models are characterized by clear interpretation. However, due to the complex mixture mechanism and limited nonlinearity modeling capacity, these models may not be accurate, especially, in analyzing scenes with unknown physical characteristics. Data-driven methods have developed rapidly in recent years, in particular deep learning methods as they possess superior capability in modeling complex and nonlinear systems. Simply transferring these methods as black-boxes to conduct unmixing may lead to low physical interpretability and generalization ability. Consequently, several contributions have been dedicated to integrating advantages of both physics-based models and data-driven methods. In this article, we present an overview of recent advances on this topic from several aspects, including deep neural network (DNN) structures design, prior capturing and loss design, and summarise these methods in a common mathematical optimization framework. In addition, relevant remarks and discussions are conducted made for providing further understanding and prospective improvement of the methods. The related source codes and data are collected and made available at http://github.com/xiuheng-wang/awesome-hyperspectral-image-unmixing.

* IEEE Signal Process. Mag. Manuscript submitted March 14, 2022 
  
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