Results 11 to 20 of about 146,074 (263)
Proceedings of the International AAAI Conference on Web and Social Media, 2021 Managing large collections of documents is an important problem for many areas of science, industry, and culture. Probabilistic topic modeling offers a promising solution. Topic modeling is an unsupervised machine learning method that learns the underlying themes in a large collection of otherwise unorganized documents.
Allison June-Barlow Chaney +1 more
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CoRR, 2021 We propose an Exclusive Topic Modeling (ETM) for unsupervised text classification, which is able to 1) identify the field-specific keywords though less frequently appeared and 2) deliver well-structured topics with exclusive words. In particular, a weighted Lasso penalty is imposed to reduce the dominance of the frequently appearing yet less relevant ...
Hao Lei, Ying Chen
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Time Series Impact Through Topic Modeling
IEEE Access, 2022 A time-series of numerical data and a sequence of time-ordered documents are often correlated. This paper aims at modeling the impact that the underlying themes discussed in the text data have on the time series.
Julian Cendrero +3 more
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Automated Audio Captioning With Topic Modeling
IEEE Access, 2023 Automatic audio captioning (AAC) is an important area of research aimed at generating meaningful descriptions for audio clips. Most existing methods use relevant semantic information to improve AAC performance and have demonstrated the feasibility of ...
Aysegul Ozkaya Eren, Mustafa Sert
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Topic Modeling for Makerspace Artifact Analysis
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2021 As the making phenomenon becomes more prevalent, diverse, and vast, it becomes increasingly challenging to identify general temporal or spatial trends in types of making endeavors.
David Wilson +2 more
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, 2012 We introduce the topical relevance model (TRLM) as a generalization of the standard relevance model (RLM). The TRLM alleviates the limitations of the RLM by exploiting the multi-topical structure of pseudo-relevant documents. In TRLM, intra-topical document and query term co-occurrences are favoured, whereas the inter-topical ones are down-weighted ...
Ganguly, Debasis +2 more
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Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022 We propose a new problem called coordinated topic modeling that imitates human behavior while describing a text corpus. It considers a set of well-defined topics like the axes of a semantic space with a reference representation. It then uses the axes to model a corpus for easily understandable representation. This new task helps represent a corpus more
Pritom Saha Akash +2 more
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Topic Modeling in Embedding Spaces
Transactions of the Association for Computational Linguistics, 2020 Topic modeling analyzes documents to learn meaningful patterns of words. However, existing topic models fail to learn interpretable topics when working with large and heavy-tailed vocabularies.
Dieng, Adji B. +2 more
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Understanding Cybersecurity Threat Trends Through Dynamic Topic Modeling
Frontiers in Big Data, 2021 Cybersecurity threats continue to increase and are impacting almost all aspects of modern life. Being aware of how vulnerabilities and their exploits are changing gives helpful insights into combating new threats.
Jennifer Sleeman +2 more
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Conceptualization Topic Modeling [PDF]
CoRR, 2017 Recently, topic modeling has been widely used to discover the abstract topics in text corpora. Most of the existing topic models are based on the assumption of three-layer hierarchical Bayesian structure, i.e. each document is modeled as a probability distribution over topics, and each topic is a probability distribution over words.
Yi-Kun Tang +3 more
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