Results 151 to 160 of about 254,803 (193)

Topic labeled text classification

Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, 2014
Supervised text classifiers require extensive human expertise and labeling efforts. In this paper, we propose a weakly supervised text classification algorithm based on the labeling of Latent Dirichlet Allocation (LDA) topics. Our algorithm is based on the generative property of LDA.
Swapnil Hingmire, Sutanu Chakraborti
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Automatically Labelled Software Topic Model

International Journal of Open Source Software and Processes, 2020
Public software repositories (SR) maintain a massive amount of valuable data offering opportunities to support software engineering (SE) tasks. Researchers have applied information retrieval techniques in mining software repositories. Topic models are one of these techniques.
Youcef Bouziane, Mustapha Kamel Abdi, Salah Sadou   +2 more
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Automatic labeling hierarchical topics

Proceedings of the 21st ACM international conference on Information and knowledge management, 2012
Recently, statistical topic modeling has been widely applied in text mining and knowledge management due to its powerful ability. A topic, as a probability distribution over words, is usually difficult to be understood. A common, major challenge in applying such topic models to other knowledge management problem is to accurately interpret the meaning ...
Xian-Ling Mao, Zhao-Yan Ming, Zheng-Jun Zha, Tat-Seng Chua, Hongfei Yan, Xiaoming Li   +5 more
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Off-label Uses of Topical Pimecrolimus

Journal of Cutaneous Medicine and Surgery, 2019
Pimecrolimus is a topical calcineurin inhibitor currently approved for second-line use in the management of mild-to-moderate atopic dermatitis in patients age 2 years and older. Given the safety profile and nonsteroidal mechanism of pimecrolimus, there has been significant interest in its use in the treatment of a variety of dermatological conditions.
Matthew Ladda, Vijay Sandhu, Arvin Ighani, Jensen Yeung   +3 more
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Automatic Topic Labeling Using Ontology-Based Topic Models

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 2015
Topic models, which frequently represent topics as multinomial distributions over words, have been extensively used for discovering latent topics in text corpora. Topic labeling, which aims to assign meaningful labels for discovered topics, has recently gained significant attention.
Mehdi Allahyari, Krys Kochut
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Multi-Label Topic Model Conditioned on Label Embedding

2019 IEEE International Conference on Computer Science and Educational Informatization (CSEI), 2019
In most real-world document collections, there are various types of labels that usually carry context information, such as label hierarchies or textual descriptions. Nonetheless, the commonly-used approaches to modeling text corpora ignore this information.
Lin Tang, Lin Liu, Jianhou Gan
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Document classification by topic labeling

Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, 2013
In this paper, we propose Latent Dirichlet Allocation (LDA) [1] based document classification algorithm which does not require any labeled dataset. In our algorithm, we construct a topic model using LDA, assign one topic to one of the class labels, aggregate all the same class label topics into a single topic using the aggregation property of the ...
Swapnil Hingmire, Sandeep Chougule, Girish K. Palshikar, Sutanu Chakraborti   +3 more
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Automatically Labelling Sentiment-Bearing Topics with Descriptive Sentence Labels [PDF]

, 2017
In this paper, we propose a simple yet effective approach for automatically labelling sentiment-bearing topics with descriptive sentence labels. Specifically, our approach consists of two components: (i) a mechanism which can automatically learn the relevance to sentiment-bearing topics of the underlying sentences in a corpus; and (ii) a sentence ...
Barawi, Mohamad Hardyman, Lin, Chenghua, Siddharthan, Advaith   +2 more
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Multi-label Classification via Label-Topic Pairs

2018
The task of learning from multi-label example is rather challenging because of the tremendous number of possible label sets. It has been well recognized that exploiting label relationships in a proper way can facilitate the learning process and boost the learning performance.
Gang Chen, Yue Peng, Chongjun Wang
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A Label Distribution Topic Model for Multi-label Classification

Proceedings of the 2019 4th International Conference on Intelligent Information Processing, 2019
At present, multi-label supervised topic model is a kind of effective multi-label classification model applied to various domain. However, due to the limitation of traditional label-topic correspondence in existing multi-label supervised topic model, there are still some aspects that need to be improved.
Lin Liu, Lin Tang
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