Results 21 to 30 of about 52,118 (285)
Automatic Labeling of Topic Models Using Graph-Based Ranking
IEEE Access, 2019 Generated topic label, an alternative representation of topics learned by topic model, is widely used to help the user interpret the topics more efficiently. A major challenge now is to label a discovered topic accurately in an objective way.
Dongbin He +4 more
doaj +3 more sources
Labeled Interactive Topic Models
CoRR, 2023 Topic models are valuable for understanding extensive document collections, but they don't always identify the most relevant topics. Classical probabilistic and anchor-based topic models offer interactive versions that allow users to guide the models ...
Boyd-Graber, Jordan +2 more
core +2 more sources
Automatic labeling of multinomial topic models
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, 2007 Multinomial distributions over words are frequently used to model topics in text collections. A common, major chal-lenge in applying all such topic models to any text mining problem is to label a multinomial topic model accurately so that a user can ...
Xuehua Shen +2 more
core +2 more sources
Selecting the Number and Labels of Topics in Topic Modeling: A Tutorial
Advances in Methods and Practices in Psychological Science, 2023 Topic modeling is a type of text analysis that identifies clusters of co-occurring words, or latent topics. A challenging step of topic modeling is determining the number of topics to extract. This tutorial describes tools researchers can use to identify
Sara J. Weston +3 more
doaj +3 more sources
United We Stand: Using Multiple Strategies for Topic Labeling
Lecture Notes in Computer Science, 2018 International audienceTopic labeling aims at providing a sound, possibly multi-words, label that depicts a topic drawn from a topic model. This is of the utmost practical interest in order to quickly grasp a topic informa-tional content-the usual ranked ...
Julien Velcin +2 more
exaly +2 more sources
Unsupervised Multi-Topic Labeling for Spoken Utterances
, 2019 Systems such as Alexa, Cortana, and Siri appear rather smart. However, they only react to predefined wordings and do not actually grasp the user\u27s intent. To overcome this limitation, a system must grasp the topics the user is talking about. Therefore,
Sebastian Weigelt +2 more
exaly +3 more sources
Labeling results of topic models: word sense disambiguation as key method for automatic topic labeling with GermaNet [PDF]
Proceedings of the Language Resources and Evaluation ConferenceThe combination of topic modeling and automatic topic labeling sheds light on understanding large corpora of text. It can be used to add semantic information for existing metadata. In addition, one can use the documents and the corresponding topic labels
Ecker, Jennifer
core +3 more sources
International Journal of Mathematics for Industry, 2022 [Formula: see text]-labeling problem ([Formula: see text]-[Formula: see text]) is an important topic in discrete mathematics due to its various applications, like in frequency assignment in mobile communication systems, signal processing, circuit design,
Sk. Amanathulla +2 more
doaj +2 more sources
Opportunities and challenges of proximity labeling for microbe-host cell interactions in tumor microenvironment [PDF]
Frontiers in Cellular and Infection MicrobiologyIn the tumor immune microenvironment, microbes promote tumor progression and metastasis by invading host cancer cells. Blocking these interactions is expected to provide new strategies for inhibiting tumor progression and metastasis, as well as opening ...
Shuang Qiu +9 more
doaj +2 more sources
Readitopics: Make Your Topic Models Readable via Labeling and Browsing [PDF]
, 2018 International audienceReaditopics provides a new tool for browsing a textual corpus that showcases several recent work for labeling topic models and estimating topic coherence.
Velcin, Julien +5 more
core +5 more sources