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HighTech and Innovation JournalDigital technologies have been used for a vast amount of bibliometric analysis research. Although these technologies have made scientific investigation more accessible and efficient, scholars now face the daunting task of sifting through an overwhelming ...
Lan Thi Nguyen +7 more
doaj +1 more source
HiTR: Hierarchical Topic Model Re-estimation for Measuring Topical Diversity of Documents [PDF]
arXiv, 2018 A high degree of topical diversity is often considered to be an important characteristic of interesting text documents. A recent proposal for measuring topical diversity identifies three distributions for assessing the diversity of documents: distributions of words within documents, words within topics, and topics within documents.
arxiv
Integration of Neural Embeddings and Probabilistic Models in Topic Modeling
Applied Artificial IntelligenceTopic modeling, a way to find topics in large volumes of text, has grown with the help of deep learning. This paper presents two novel approaches to topic modeling by integrating embeddings derived from Bert-Topic with the multi-grain clustering topic ...
Pantea Koochemeshkian, Nizar Bouguila
doaj +1 more source
Book Review: Model theoretic algebra: Selected topics [PDF]
, 1977 Paul C. Eklof
openalex +1 more source
An Online Topic Modeling Framework with Topics Automatically Labeled [PDF]
arXiv, 2019 In this paper, we propose a novel online topic tracking framework, named IEDL, for tracking the topic changes related to deep learning techniques on Stack Exchange and automatically interpreting each identified topic. The proposed framework combines the prior topic distributions in a time window during inferring the topics in current time slice, and ...
arxiv
A computational approach to topic and focus in a production model [PDF]
, 1988 Vincenza Pignataro
openalex +1 more source
Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling [PDF]
arXivLarge language models (LLMs) with their strong zero-shot topic extraction capabilities offer an alternative to probabilistic topic modelling and closed-set topic classification approaches. As zero-shot topic extractors, LLMs are expected to understand human instructions to generate relevant and non-hallucinated topics based on the given documents ...
arxiv
Integrating Document Clustering and Topic Modeling [PDF]
arXiv, 2013 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 topics specific to each cluster and ...
arxiv
Lifelong Neural Topic Learning in Contextualized Autoregressive Topic Models of Language via Informative Transfers [PDF]
arXiv, 2019 Topic models such as LDA, DocNADE, iDocNADEe have been popular in document analysis. However, the traditional topic models have several limitations including: (1) Bag-of-words (BoW) assumption, where they ignore word ordering, (2) Data sparsity, where the application of topic models is challenging due to limited word co-occurrences, leading to ...
arxiv