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"Topic Modeling": models, code, and papers

Social-Media Activity Forecasting with Exogenous Information Signals

Sep 22, 2021
Kin Wai Ng, Sameera Horawalavithana, Adriana Iamnitchi

Due to their widespread adoption, social media platforms present an ideal environment for studying and understanding social behavior, especially on information spread. Modeling social media activity has numerous practical implications such as supporting efforts to analyze strategic information operations, designing intervention techniques to mitigate disinformation, or delivering critical information during disaster relief operations. In this paper we propose a modeling technique that forecasts topic-specific daily volume of social media activities by using both exogenous signals, such as news or armed conflicts records, and endogenous data from the social media platform we model. Empirical evaluations with real datasets from two different platforms and two different contexts each composed of multiple interrelated topics demonstrate the effectiveness of our solution.

  

Text Similarity in Vector Space Models: A Comparative Study

Sep 24, 2018
Omid Shahmirzadi, Adam Lugowski, Kenneth Younge

Automatic measurement of semantic text similarity is an important task in natural language processing. In this paper, we evaluate the performance of different vector space models to perform this task. We address the real-world problem of modeling patent-to-patent similarity and compare TFIDF (and related extensions), topic models (e.g., latent semantic indexing), and neural models (e.g., paragraph vectors). Contrary to expectations, the added computational cost of text embedding methods is justified only when: 1) the target text is condensed; and 2) the similarity comparison is trivial. Otherwise, TFIDF performs surprisingly well in other cases: in particular for longer and more technical texts or for making finer-grained distinctions between nearest neighbors. Unexpectedly, extensions to the TFIDF method, such as adding noun phrases or calculating term weights incrementally, were not helpful in our context.

* 17 pages 
  

Multi Sense Embeddings from Topic Models

Sep 17, 2019
Shobhit Jain, Sravan Babu Bodapati, Ramesh Nallapati, Anima Anandkumar

Distributed word embeddings have yielded state-of-the-art performance in many NLP tasks, mainly due to their success in capturing useful semantic information. These representations assign only a single vector to each word whereas a large number of words are polysemous (i.e., have multiple meanings). In this work, we approach this critical problem in lexical semantics, namely that of representing various senses of polysemous words in vector spaces. We propose a topic modeling based skip-gram approach for learning multi-prototype word embeddings. We also introduce a method to prune the embeddings determined by the probabilistic representation of the word in each topic. We use our embeddings to show that they can capture the context and word similarity strongly and outperform various state-of-the-art implementations.

* ACL, Year: 2019, Volume: 74, Page: 42 
* 8 pages, 1 figure, 7 tables 
  

Leveraging Natural Learning Processing to Uncover Themes in Clinical Notes of Patients Admitted for Heart Failure

Apr 14, 2022
Ankita Agarwal, Krishnaprasad Thirunarayan, William L. Romine, Amanuel Alambo, Mia Cajita, Tanvi Banerjee

Heart failure occurs when the heart is not able to pump blood and oxygen to support other organs in the body as it should. Treatments include medications and sometimes hospitalization. Patients with heart failure can have both cardiovascular as well as non-cardiovascular comorbidities. Clinical notes of patients with heart failure can be analyzed to gain insight into the topics discussed in these notes and the major comorbidities in these patients. In this regard, we apply machine learning techniques, such as topic modeling, to identify the major themes found in the clinical notes specific to the procedures performed on 1,200 patients admitted for heart failure at the University of Illinois Hospital and Health Sciences System (UI Health). Topic modeling revealed five hidden themes in these clinical notes, including one related to heart disease comorbidities.

* 4 pages, 2 tables, accepted in IEEE EMBC 2022 conference (IEEE International Engineering in Medicine and Biology Conference) 
  

Online Bayesian Collaborative Topic Regression

May 28, 2016
Chenghao Liu, Tao Jin, Steven C. H. Hoi, Peilin Zhao, Jianling Sun

Collaborative Topic Regression (CTR) combines ideas of probabilistic matrix factorization (PMF) and topic modeling (e.g., LDA) for recommender systems, which has gained increasing successes in many applications. Despite enjoying many advantages, the existing CTR algorithms have some critical limitations. First of all, they are often designed to work in a batch learning manner, making them unsuitable to deal with streaming data or big data in real-world recommender systems. Second, the document-specific topic proportions of LDA are fed to the downstream PMF, but not reverse, which is sub-optimal as the rating information is not exploited in discovering the low-dimensional representation of documents and thus can result in a sub-optimal representation for prediction. In this paper, we propose a novel scheme of Online Bayesian Collaborative Topic Regression (OBCTR) which is efficient and scalable for learning from data streams. Particularly, we {\it jointly} optimize the combined objective function of both PMF and LDA in an online learning fashion, in which both PMF and LDA tasks can be reinforced each other during the online learning process. Our encouraging experimental results on real-world data validate the effectiveness of the proposed method.

  

Noninvasive Fetal Electrocardiography: Models, Technologies and Algorithms

Dec 24, 2021
Reza Sameni

The fetal electrocardiogram (fECG) was first recorded from the maternal abdominal surface in the early 1900s. During the past fifty years, the most advanced electronics technologies and signal processing algorithms have been used to convert noninvasive fetal electrocardiography into a reliable technology for fetal cardiac monitoring. In this chapter, the major signal processing techniques, which have been developed for the modeling, extraction and analysis of the fECG from noninvasive maternal abdominal recordings are reviewed and compared with one another in detail. The major topics of the chapter include: 1) the electrophysiology of the fECG from the signal processing viewpoint, 2) the mathematical model of the maternal volume conduction media and the waveform models of the fECG acquired from body surface leads, 3) the signal acquisition requirements, 4) model-based techniques for fECG noise and interference cancellation, including adaptive filters and semi-blind source separation techniques, and 5) recent algorithmic advances for fetal motion tracking and online fECG extraction from few number of channels.

* In Innovative Technologies and Signal Processing in Perinatal Medicine (pp. 99-146). Springer International Publishing (2020) 
  

Continual Learning of Long Topic Sequences in Neural Information Retrieval

Jan 10, 2022
Thomas Gerald, Laure Soulier

In information retrieval (IR) systems, trends and users' interests may change over time, altering either the distribution of requests or contents to be recommended. Since neural ranking approaches heavily depend on the training data, it is crucial to understand the transfer capacity of recent IR approaches to address new domains in the long term. In this paper, we first propose a dataset based upon the MSMarco corpus aiming at modeling a long stream of topics as well as IR property-driven controlled settings. We then in-depth analyze the ability of recent neural IR models while continually learning those streams. Our empirical study highlights in which particular cases catastrophic forgetting occurs (e.g., level of similarity between tasks, peculiarities on text length, and ways of learning models) to provide future directions in terms of model design.

  

Topic Community Based Temporal Expertise for Question Routing

Jul 05, 2022
Vaibhav Krishna, Vaiva Vasiliauskaite, Nino Antulov-Fantulin

Question Routing in Community-based Question Answering websites aims at recommending newly posted questions to potential users who are most likely to provide "accepted answers". Most of the existing approaches predict users' expertise based on their past question answering behavior and the content of new questions. However, these approaches suffer from challenges in three aspects: 1) sparsity of users' past records results in lack of personalized recommendation that at times does not match users' interest or domain expertise, 2) modeling based on all questions and answers content makes periodic updates computationally expensive, and 3) while CQA sites are highly dynamic, they are mostly considered as static. This paper proposes a novel approach to QR that addresses the above challenges. It is based on dynamic modeling of users' activity on topic communities. Experimental results on three real-world datasets demonstrate that the proposed model significantly outperforms competitive baseline models

  

Gaussian Determinantal Processes: a new model for directionality in data

Nov 19, 2021
Subhro Ghosh, Philippe Rigollet

Determinantal point processes (a.k.a. DPPs) have recently become popular tools for modeling the phenomenon of negative dependence, or repulsion, in data. However, our understanding of an analogue of a classical parametric statistical theory is rather limited for this class of models. In this work, we investigate a parametric family of Gaussian DPPs with a clearly interpretable effect of parametric modulation on the observed points. We show that parameter modulation impacts the observed points by introducing directionality in their repulsion structure, and the principal directions correspond to the directions of maximal (i.e. the most long ranged) dependency. This model readily yields a novel and viable alternative to Principal Component Analysis (PCA) as a dimension reduction tool that favors directions along which the data is most spread out. This methodological contribution is complemented by a statistical analysis of a spiked model similar to that employed for covariance matrices as a framework to study PCA. These theoretical investigations unveil intriguing questions for further examination in random matrix theory, stochastic geometry and related topics.

* Proceedings of the National Academy of Sciences 117, no. 24 (2020): 13207-13213 
* Published in the Proceedings of the National Academy of Sciences (Direct Submission) 
  

Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning

May 04, 2021
Xiangyu Peng, Siyan Li, Sarah Wiegreffe, Mark Riedl

Transformer-based language model approaches to automated story generation currently provide state-of-the-art results. However, they still suffer from plot incoherence when generating narratives over time, and critically lack basic commonsense reasoning. Furthermore, existing methods generally focus only on single-character stories, or fail to track characters at all. To improve the coherence of generated narratives and to expand the scope of character-centric narrative generation, we introduce Commonsense-inference Augmented neural StoryTelling (CAST), a framework for introducing commonsense reasoning into the generation process while modeling the interaction between multiple characters. We find that our CAST method produces significantly more coherent and on-topic two-character stories, outperforming baselines in dimensions including plot plausibility and staying on topic. We also show how the CAST method can be used to further train language models that generate more coherent stories and reduce computation cost.

  
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