نتایج جستجو برای: topic modeling
تعداد نتایج: 543140 فیلتر نتایج به سال:
Interactive topic models are powerful tools for understanding large collections of text. However, existing sampling-based interactive topic modeling approaches scale poorly to large data sets. Anchor methods, which use a single word to uniquely identify a topic, offer the speed needed for interactive work but lack both a mechanism to inject prior knowledge and lack the intuitive semantics neede...
This paper investigates the problem of finding author interest in coauthor network through topic modeling with providing several performance evaluation measures. Intuitively, there are two types of explicit grouping exists in research papers (1) authors who have co-authored with author A in one document (subgroup) and (2) authors who have co-authored with author A in all documents (group). Trad...
We address the problem of combining topic modeling with sentiment analysis within a generative model. While the Hierarchical Dirichlet Process (HDP) has seen recent widespread use for topic modeling alone, most current hybrid models for concurrent inference of sentiments and topics are not based on HDP. In this paper, we present HDPsent, a new model which incorporates Latent Dirichlet Allocatio...
Latent Dirichlet allocation (LDA) is a widely-used probabilistic topic modeling paradigm, and recently finds many applications in computer vision and computational biology. In this paper, we propose a fast and accurate batch algorithm, active belief propagation (ABP), for training LDA. Usually batch LDA algorithms require repeated scanning of the entire corpus and searching the complete topic s...
Bibliometric analysis is a research method used in library and information science to evaluate research performance. It applies quantitative and statistical analyses to describe patterns observed in a set of publications and can help identify previous, current, and future research trends or focus. To better guide our institutional strategic plan in cancer population science, we conducted biblio...
Topic modeling and word embedding are two important techniques for deriving latent semantics from data. General-purpose topic models typically work in coarse granularity by capturing word co-occurrence at the document/sentence level. In contrast, word embedding models usually work in fine granularity by modeling word co-occurrence within small sliding windows. With the aim of deriving latent se...
Understanding the content of a large text corpus can be assisted by topic modeling methods, but the discovered topics often do not make clear sense to human analysts. Interactive topic modeling addresses such problems by allowing a human to steer the topic model curation process (generate, interpret, diagnose, and refine). However, human have limited ability to work with the artifacts of comput...
Online education provides data from students solving problems at different levels of proficiency over time. Unfortunately, methods that use these data for inferring student knowledge rely on costly domain expertise. We propose three novel data-driven methods that bridge sequence modeling with topic models to infer students’ time varying knowledge. These methods differ in complexity, interpretab...
A new approach is proposed for topic modeling, in which the latent matrix factorization employs Gaussian priors, rather than the Dirichlet-class priors widely used in such models. The use of a latent-Gaussian model permits simple and efficient approximate Bayesian posterior inference, via the Laplace approximation. On multiple datasets, the proposed approach is demonstrated to yield results as ...
This paper studies how to incorporate the external word correlation knowledge to improve the coherence of topic modeling. Existing topic models assume words are generated independently and lack the mechanism to utilize the rich similarity relationships among words to learn coherent topics. To solve this problem, we build a Markov Random Field (MRF) regularized Latent Dirichlet Allocation (LDA) ...
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