Topic-aware chatbot learns from NMF topic vectors.
problem Improving chatbot relevance based on user topics.
method Combines RNN with NMF for topic learning and attention.
result Chatbot provides more relevant answers based on topic.
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48 results for “topic”
The top word list, i.e., the top-M words with highest marginal probability in a given topic, is the standard topic representation in topic models. Most of recent automatical topic labeling algorithms and popular topic quality metrics are based on it. However, we find, empirically, words in this type of top word list ar…
A new model CDTM improves text classification by concentrating document topics.
problem Unsupervised text classification with diverse topic distributions.
method Imposes an exponential entropy penalty on document topic distribution to encourage concentration.
result More coherent topics and concentrated, sparse document-topic distributions.
New metric correlates local topic quality with human judgments.
problem Evaluation of topic models focuses on global metrics, ignoring token-level assignments.
method Proposed a human evaluation task and automated metrics to assess local topic quality.
result Consistency metric correlates best with human judgments of local topic quality.
Document clustering and topic modeling are two closely related tasks which can mutually benefit each other. Topic modeling can project documents into a topic space which facilitates effective document clustering. Cluster labels discovered by document clustering can be incorporated into topic models to extract local top…
ETM discovers interpretable topics in large vocabularies.
problem Existing topic models fail with large, heavy-tailed vocabularies.
method Generative model combining topic models and word embeddings with variational inference.
result ETM discovers interpretable topics even with large vocabularies.
Topic models are probabilistic models for discovering topical themes in collections of documents. In real world applications, these models provide us with the means of organizing what would otherwise be unstructured collections. They can help us cluster a huge collection into different topics or find a subset of the co…
Recently, considerable research effort has been devoted to developing deep architectures for topic models to learn topic structures. Although several deep models have been proposed to learn better topic proportions of documents, how to leverage the benefits of deep structures for learning word distributions of topics h…
Predictive topic models retain only relevant terms for better prediction and topic coherence.
problem Misspecification of topic models leads to poor prediction and topic coherence.
method Uses supervisory signal to select vocabulary terms improving prediction performance.
result Prediction-focused topic models learn more coherent topics while maintaining competitive predictions.
Paper compares NMF and LDA for topic labeling in customer communications.
problem Automatically labeling topics in customer inquiries.
method Uses NMF and LDA for topic mining and labeling.
result Proposes methods for automated topic subject labeling.
Social media conversations unfold based on complex interactions between users, topics and time. While recent models have been proposed to capture network strengths between users, users' topical preferences and temporal patterns between posting and response times, interaction patterns between topics has not been studied…
We introduce Gaussian Process Topic Models (GPTMs), a new family of topic models which can leverage a kernel among documents while extracting correlated topics. GPTMs can be considered a systematic generalization of the Correlated Topic Models (CTMs) using ideas from Gaussian Process (GP) based embedding. Since GPTMs w…
New metric improves topic model evaluation.
problem Lack of standard cross-contextual evaluation metrics for topic modeling.
method Introduces as a coefficient of determination for topic models.
result Improves topic model evaluation by providing a standard metric.
AOBTM adapts online topic modeling for short app reviews, revealing coherent topics over time.
problem Challenges in inferring latent topics from short, dynamic app reviews over multiple versions.
method Adaptive Online Biterm Topic Model (AOBTM) that addresses sparsity and considers statistical data from previous versions.
result AOBTM finds more coherent topics and outperforms state-of-the-art baselines.
Neural topic models improve topic discovery in large corpora.
problem Topic discovery in large text corpora.
method Neural auto-encoder based document embedding with PLSA assumptions.
result Neural topic models outperform LDA in capturing relevant topics and perplexity.
Paper proposes a method to monitor research topic evolution.
problem Difficulty in tracking research topic diffusion and evolution.
method Deep Non-negative Autoencoder with information divergence measurement.
result Identifies evolution of research topics and discovers topic diffusions.
This paper improves topic modeling by embedding words and topics together.
problem Topic models struggle with short documents and approximate inference.
method Model each document as a mixture of word embeddings and each topic as a mixture of topic embeddings.
result The method optimizes topic embeddings to minimize semantic differences between words and topics.
A new neural topic model using optimal transport improves document representation and topic coherence.
problem Challenges in achieving good document representation and coherent/diverse topics in existing NTMs.
method Proposes a neural topic model via optimal transport, learning topic distribution by minimising OT distance to document word distributions.
result Significantly outperforms state-of-the-art NTMs on discovering coherent and diverse topics.
Scientific documents rely on both mathematics and text to communicate ideas. Inspired by the topical correspondence between mathematical equations and word contexts observed in scientific texts, we propose a novel topic model that jointly generates mathematical equations and their surrounding text (TopicEq). Using an e…
We propose a parsimonious topic model for text corpora. In related models such as Latent Dirichlet Allocation (LDA), all words are modeled topic-specifically, even though many words occur with similar frequencies across different topics. Our modeling determines salient words for each topic, which have topic-specific pr…
New Gamma-Poisson model improves topic selection for short text.
problem Topic modelling for short text using Poisson distribution.
method Gamma-Poisson mixture model with collapsed Gibbs sampler.
result Gamma-Poisson model selects more accurate number of topics.
E-LDA: Toward Interpretable LDA Topic Models with Strong Guarantees in Logarithmic Parallel Timecs.LG
E-LDA offers faster, interpretable LDA topic models.
problem Inferring topics in LDA topic models with strong guarantees.
method Non-gradient combinatorial approach for faster convergence.
result Logarithmic parallel computation time and interpretability.
CFTM uses fractional Brownian motion for dynamic topic modeling.
problem Identifying long-term dependency or roughness in topic and word distributions over time.
method Continuous Time Fractional Topic Model (cFTM) incorporating fractional Brownian motion.
result cFTM captures long-term dependency or roughness in topic and word distributions.
We describe our language-independent unsupervised word sense induction system. This system only uses topic features to cluster different word senses in their global context topic space. Using unlabeled data, this system trains a latent Dirichlet allocation (LDA) topic model then uses it to infer the topics distribution…
Geometric method captures rare topics and temporal alignment in co-author networks.
problem Missing rare topics and smooth temporal alignment in topic modeling.
method Integrates multimodal text and co-author network data using Hellinger distances and Ward's linkage.
result Effective identification of rare topics and visualization of topic drift over time.
Predicts EHR features that improve prediction, learning coherent topics.
problem Balancing prediction quality and coherence in EHR topic models.
method Prediction-focused topic model that uses supervisory signal to retain relevant features.
result Prediction-focused topic models learn more coherent topics while maintaining competitive predictions.
Faster, context-aware topic modeling approach unveiled.
problem Slow and heavy preprocessing in topic modeling.
method Conceptualizes topics as independent axes, decomposes embeddings using ICA.
result 4.5x faster than BERTopic on average, with diverse and coherent topics.
A dynamic keyword selection model for topic modeling of tweets.
problem Adjusting keywords dynamically to mimic past topics with novelty.
method Generative process selects keywords and documents, trained with variational lower bound and stochastic gradient optimization.
result Keyword-based topic model outperforms a sophisticated baseline model by 67%.
We introduce the author-topic model, a generative model for documents that extends Latent Dirichlet Allocation (LDA; Blei, Ng, & Jordan, 2003) to include authorship information. Each author is associated with a multinomial distribution over topics and each topic is associated with a multinomial distribution over words.…
Improved neural topic model for semi-supervised learning.
problem Representing textual data in an interpretable manner with limited labeled data.
method Label-Indexed Neural Topic Model (LI-NTM) that combines deep generative models with semi-supervised learning.
result LI-NTM outperforms existing models in document reconstruction and classifier performance.
Hierarchical latent tree analysis (HLTA) is recently proposed as a new method for topic detection. It differs fundamentally from the LDA-based methods in terms of topic definition, topic-document relationship, and learning method. It has been shown to discover significantly more coherent topics and better topic hierarc…
Topic Extraction of Crawled Documents Collection using Correlated Topic Model in MapReduce Frameworkcs.IR
A scalable topic model for large document collections using MapReduce.
problem Scalability issues in topic modeling for large document collections.
method Correlated Topic Model with variational Expectation-Maximization in MapReduce framework.
result Comparable topic coherences with LDA in MapReduce framework.
New NMF algorithms improve topic model fits.
problem Improving topic model fits for large datasets.
method Leveraging recent NMF optimization methods to fit topic models efficiently.
result Better topic model fits and faster computation times.
Enhances topic-metadata relationship modeling using Bayesian methods.
problem Estimating relationships between latent topics and metadata in topic modeling.
method Proposes modifications to the method of composition, using Beta regression and a fully Bayesian approach.
result Improves quantification of uncertainty in topic-metadata relationships.
We propose new algorithms for topic modeling when the number of topics is unknown. Our approach relies on an analysis of the concentration of mass and angular geometry of the topic simplex, a convex polytope constructed by taking the convex hull of vertices representing the latent topics. Our algorithms are shown in pr…
Correlated topic modeling has been limited to small model and problem sizes due to their high computational cost and poor scaling. In this paper, we propose a new model which learns compact topic embeddings and captures topic correlations through the closeness between the topic vectors. Our method enables efficient inf…
Paper proposes a Renyi entropy-based method for tuning hierarchical topic models.
problem Tuning hierarchical topic models, especially determining the number of topics at each level, is challenging.
method The paper introduces a Renyi entropy-based metric for quality assessment and a practical tuning concept.
result The proposed method can estimate the number of topics for two hierarchical levels in hARTM model.
We present a new topic model that generates documents by sampling a topic for one whole sentence at a time, and generating the words in the sentence using an RNN decoder that is conditioned on the topic of the sentence. We argue that this novel formalism will help us not only visualize and model the topical discourse s…
nnLDA combines neural and probabilistic methods for better topic modeling with side information.
problem Lack of integration of auxiliary information in traditional topic models.
method nnLDA integrates side information through a neural prior mechanism, optimizing both neural and probabilistic components.
result nnLDA outperforms traditional models in topic coherence, perplexity, and classification.
Due to recent explosion of text data, researchers have been overwhelmed by ever-increasing volume of articles produced by different research communities. Various scholarly search websites, citation recommendation engines, and research databases have been created to simplify the text search tasks. However, it is still d…
A Constrained Coupled Matrix-Tensor Factorization for Learning Time-evolving and Emerging Topicscs.IR
Topic discovery has witnessed a significant growth as a field of data mining at large. In particular, time-evolving topic discovery, where the evolution of a topic is taken into account has been instrumental in understanding the historical context of an emerging topic in a dynamic corpus. Traditionally, time-evolving t…
Neural model predicts survival outcomes and reveals feature relationships.
problem Predicting time-to-event outcomes and understanding feature relationships in clinical data.
method Survival and topic modeling combined in a neural network framework.
result Neural survival-supervised topic models achieve competitive accuracy with interpretability.
TACAM improves argument mining by integrating topic and external context.
problem Mining arguments from text without topic information leads to confusion.
method Proposes models that consider topic information and integrate external context.
result Performance boost for argument mining when topic and external context are considered.
Top2Vec finds topic vectors from documents and words without needing stop words or custom settings.
problem Topic modeling weaknesses, including needing known topics, stop words, and custom settings.
method Joint document and word semantic embedding to find topic vectors automatically.
result Top2Vec finds more informative and representative topics than probabilistic models.
In this technical report, we present jLDADMM---an easy-to-use Java toolkit for conventional topic models. jLDADMM is released to provide alternatives for topic modeling on normal or short texts. It provides implementations of the Latent Dirichlet Allocation topic model and the one-topic-per-document Dirichlet Multinomi…
Interactive topic models with user-defined anchor words.
problem Building interpretable topic models.
method Protocol allowing users to interactively define anchor words for topic modeling.
result Validation of the usefulness of interactive topic models.
Paper presents a novel neural network method for topic detection in micro-blogs.
problem Challenging topic detection without known topic count.
method Unsupervised neural sentence embedding model with attention mechanism.
result Improved clustering algorithm (RADBSCAN) discovers topics based on dataset character.
Topic sparsity refers to the observation that individual documents usually focus on several salient topics instead of covering a wide variety of topics, and a real topic adopts a narrow range of terms instead of a wide coverage of the vocabulary. Understanding this topic sparsity is especially important for analyzing u…