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"Topic": models, code, and papers

Discriminative Relational Topic Models

Oct 09, 2013
Ning Chen, Jun Zhu, Fei Xia, Bo Zhang

Many scientific and engineering fields involve analyzing network data. For document networks, relational topic models (RTMs) provide a probabilistic generative process to describe both the link structure and document contents, and they have shown promise on predicting network structures and discovering latent topic representations. However, existing RTMs have limitations in both the restricted model expressiveness and incapability of dealing with imbalanced network data. To expand the scope and improve the inference accuracy of RTMs, this paper presents three extensions: 1) unlike the common link likelihood with a diagonal weight matrix that allows the-same-topic interactions only, we generalize it to use a full weight matrix that captures all pairwise topic interactions and is applicable to asymmetric networks; 2) instead of doing standard Bayesian inference, we perform regularized Bayesian inference (RegBayes) with a regularization parameter to deal with the imbalanced link structure issue in common real networks and improve the discriminative ability of learned latent representations; and 3) instead of doing variational approximation with strict mean-field assumptions, we present collapsed Gibbs sampling algorithms for the generalized relational topic models by exploring data augmentation without making restricting assumptions. Under the generic RegBayes framework, we carefully investigate two popular discriminative loss functions, namely, the logistic log-loss and the max-margin hinge loss. Experimental results on several real network datasets demonstrate the significance of these extensions on improving the prediction performance, and the time efficiency can be dramatically improved with a simple fast approximation method.

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A Scalable Asynchronous Distributed Algorithm for Topic Modeling

Dec 16, 2014
Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S. V. N Vishwanathan, Inderjit S. Dhillon

Learning meaningful topic models with massive document collections which contain millions of documents and billions of tokens is challenging because of two reasons: First, one needs to deal with a large number of topics (typically in the order of thousands). Second, one needs a scalable and efficient way of distributing the computation across multiple machines. In this paper we present a novel algorithm F+Nomad LDA which simultaneously tackles both these problems. In order to handle large number of topics we use an appropriately modified Fenwick tree. This data structure allows us to sample from a multinomial distribution over $T$ items in $O(\log T)$ time. Moreover, when topic counts change the data structure can be updated in $O(\log T)$ time. In order to distribute the computation across multiple processor we present a novel asynchronous framework inspired by the Nomad algorithm of \cite{YunYuHsietal13}. We show that F+Nomad LDA significantly outperform state-of-the-art on massive problems which involve millions of documents, billions of words, and thousands of topics.

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Sparse Topical Coding

Feb 14, 2012
Jun Zhu, Eric P. Xing

We present sparse topical coding (STC), a non-probabilistic formulation of topic models for discovering latent representations of large collections of data. Unlike probabilistic topic models, STC relaxes the normalization constraint of admixture proportions and the constraint of defining a normalized likelihood function. Such relaxations make STC amenable to: 1) directly control the sparsity of inferred representations by using sparsity-inducing regularizers; 2) be seamlessly integrated with a convex error function (e.g., SVM hinge loss) for supervised learning; and 3) be efficiently learned with a simply structured coordinate descent algorithm. Our results demonstrate the advantages of STC and supervised MedSTC on identifying topical meanings of words and improving classification accuracy and time efficiency.

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Covid-Transformer: Detecting Trending Topics on Twitter Using Universal Sentence Encoder

Sep 10, 2020
Meysam Asgari-Chenaghlu, Narjes Nikzad-Khasmakhi, Shervin Minaee

The novel corona-virus disease (also known as COVID-19) has led to a pandemic, impacting more than 200 countries across the globe. With its global impact, COVID-19 has become a major concern of people almost everywhere, and therefore there are a large number of tweets coming out from every corner of the world, about COVID-19 related topics. In this work, we try to analyze the tweets and detect the trending topics and major concerns of people on Twitter, which can enable us to better understand the situation, and devise better planning. More specifically we propose a model based on the universal sentence encoder to detect the main topics of Tweets in recent months. We used universal sentence encoder in order to derive the semantic representation and the similarity of tweets. We then used the sentence similarity and their embeddings, and feed them to K-means clustering algorithm to group similar tweets (in semantic sense). After that, the cluster summary is obtained using a text summarization algorithm based on deep learning, which can uncover the underlying topics of each cluster. Through experimental results, we show that our model can detect very informative topics, by processing a large number of tweets on sentence level (which can preserve the overall meaning of the tweets). Since this framework has no restriction on specific data distribution, it can be used to detect trending topics from any other social media and any other context rather than COVID-19. Experimental results show superiority of our proposed approach to other baselines, including TF-IDF, and latent Dirichlet allocation (LDA).

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Neural Attention-Aware Hierarchical Topic Model

Oct 14, 2021
Yuan Jin, He Zhao, Ming Liu, Lan Du, Wray Buntine

Neural topic models (NTMs) apply deep neural networks to topic modelling. Despite their success, NTMs generally ignore two important aspects: (1) only document-level word count information is utilized for the training, while more fine-grained sentence-level information is ignored, and (2) external semantic knowledge regarding documents, sentences and words are not exploited for the training. To address these issues, we propose a variational autoencoder (VAE) NTM model that jointly reconstructs the sentence and document word counts using combinations of bag-of-words (BoW) topical embeddings and pre-trained semantic embeddings. The pre-trained embeddings are first transformed into a common latent topical space to align their semantics with the BoW embeddings. Our model also features hierarchical KL divergence to leverage embeddings of each document to regularize those of their sentences, thereby paying more attention to semantically relevant sentences. Both quantitative and qualitative experiments have shown the efficacy of our model in 1) lowering the reconstruction errors at both the sentence and document levels, and 2) discovering more coherent topics from real-world datasets.

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Topic Detection from Conversational Dialogue Corpus with Parallel Dirichlet Allocation Model and Elbow Method

Jun 05, 2020
Haider Khalid, Vincent Wade

A conversational system needs to know how to switch between topics to continue the conversation for a more extended period. For this topic detection from dialogue corpus has become an important task for a conversation and accurate prediction of conversation topics is important for creating coherent and engaging dialogue systems. In this paper, we proposed a topic detection approach with Parallel Latent Dirichlet Allocation (PLDA) Model by clustering a vocabulary of known similar words based on TF-IDF scores and Bag of Words (BOW) technique. In the experiment, we use K-mean clustering with Elbow Method for interpretation and validation of consistency within-cluster analysis to select the optimal number of clusters. We evaluate our approach by comparing it with traditional LDA and clustering technique. The experimental results show that combining PLDA with Elbow method selects the optimal number of clusters and refine the topics for the conversation.

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Topic Modeling via Full Dependence Mixtures

Jun 13, 2019
Dan Fisher, Mark Kozdoba, Shie Mannor

We consider the topic modeling problem for large datasets. For this problem, Latent Dirichlet Allocation (LDA) with a collapsed Gibbs sampler optimization is the state-of-the-art approach in terms of topic quality. However, LDA is a slow approach, and running it on large datasets is impractical even with modern hardware. In this paper we propose to fit topics directly to the co-occurances data of the corpus. In particular, we introduce an extension of a mixture model, the Full Dependence Mixture (FDM), which arises naturally as a model of a second moment under general generative assumptions on the data. While there is some previous work on topic modeling using second moments, we develop a direct stochastic optimization procedure for fitting an FDM with a single Kullback Leibler objective. While moment methods in general have the benefit that an iteration no longer needs to scale with the size of the corpus, our approach also allows us to leverage standard optimizers and GPUs for the problem of topic modeling. We evaluate the approach on synthetic and semi-synthetic data, as well as on the SOTU and Neurips Papers corpora, and show that the approach outperforms LDA, where LDA is run on both full and sub-sampled data.

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Mapping Research Topics in Software Testing: A Bibliometric Analysis

Sep 09, 2021
Alireza Salahirad, Gregory Gay, Ehsan Mohammadi

In this study, we apply co-word analysis - a text mining technique based on the co-occurrence of terms - to map the topology of software testing research topics, with the goal of providing current and prospective researchers with a map, and observations about the evolution, of the software testing field. Our analysis enables the mapping of software testing research into clusters of connected topics, from which emerge a total of 16 high-level research themes and a further 18 subthemes. This map also suggests topics that are growing in importance, including topics related to web and mobile applications and artificial intelligence. Exploration of author and country-based collaboration patterns offers similar insight into the implicit and explicit factors that influence collaboration and suggests emerging sources of collaboration for future work. We make our observations - and the underlying mapping of research topics and research collaborations - available so that researchers can gain a deeper understanding of the topology of the software testing field, inspiration regarding new areas and connections to explore, and collaborators who will broaden their perspectives.

* Under submission to Journal of Systems and Software 

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Auto-Encoding Variational Bayes for Inferring Topics and Visualization

Oct 25, 2020
Dang Pham, Tuan M. V. Le

Visualization and topic modeling are widely used approaches for text analysis. Traditional visualization methods find low-dimensional representations of documents in the visualization space (typically 2D or 3D) that can be displayed using a scatterplot. In contrast, topic modeling aims to discover topics from text, but for visualization, one needs to perform a post-hoc embedding using dimensionality reduction methods. Recent approaches propose using a generative model to jointly find topics and visualization, allowing the semantics to be infused in the visualization space for a meaningful interpretation. A major challenge that prevents these methods from being used practically is the scalability of their inference algorithms. We present, to the best of our knowledge, the first fast Auto-Encoding Variational Bayes based inference method for jointly inferring topics and visualization. Since our method is black box, it can handle model changes efficiently with little mathematical rederivation effort. We demonstrate the efficiency and effectiveness of our method on real-world large datasets and compare it with existing baselines.

* Accepted at the 28th International Conference on Computational Linguistics (COLING 2020) 

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