Learning to classify short text from scientific documents using topic models with various types of knowledge

Vo, Duc-Thuan; Ock, Cheol-Young

doi:10.1016/j.eswa.2014.09.031

Cited by 66 publications

(29 citation statements)

References 21 publications

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“…One of the most common techniques for text transformation is enrichment, Phan et al [10] propose to use Latent Dirichelet Allocation (LDA) to generate several clusters of words (topics) from a set of external documents, and then to add the words in these topics to the texts in the training dataset based on the semantic links between the words in the texts and the words in the topics. In a similar way, Vo and Ock [19], multiply the sources of external documents to better enrich scientific document titles. In a previous work [3], we proposed a twolevel enrichment method which, on the one hand, adds to short texts words from topics generated with LDA and, on the other hand, adds words from topics according to the similarities between the topics and the texts as whole entities.…”

Section: Training Data Preprocessingmentioning

confidence: 97%

Interactive generic learning method (IGLM)

Bouaziz

Pereira

Pallez

et al. 2016

Proceedings of the 31st Annual ACM Symposium on Applied Computing

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International audienceWe propose a method to improve the performance of Random Forests for classifying short texts interactively. In short text classification, the principle of learning algorithms is to build a static model using a training dataset, then to use this model to classify new texts. Many works concentrate on improving the representation of the data as a way to build better models. We intend to tackle the problem in two ways: first by abstracting data to solve the problem of sparseness, and second by taking benefit from already classified data to continuously improve the model. Besides, in order to alleviate the amount of manual annotation, we propose an interactive method in which a manual correct annotation is required only for some misclassified texts, which are then incorporated into the training data to build an updated model. An important challenge is then to determine when to trigger this operation and how to perform the update. Applied on the standard search-snippets dataset, our method allowed a significant improvement

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Section: Training Data Preprocessingmentioning

confidence: 97%

Interactive generic learning method (IGLM)

Bouaziz

Pereira

Pallez

et al. 2016

Proceedings of the 31st Annual ACM Symposium on Applied Computing

View full text Add to dashboard Cite

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“…Text categorisation [19], vocabulary reduction [20], visual encoding [21], image recognition [22] or even video retrieval [23] are some of the applications where topic models have been successfully used.…”

Section: Current Limitations and Trendsmentioning

confidence: 99%

Sparse multi-modal probabilistic latent semantic analysis for single-image super-resolution

Fernández-Beltrán

Pla

2018

Signal Processing

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This paper presents a novel single-image super-resolution (SR) approach based on latent topics in order to take advantage of the semantics pervading the topic space when super-resolving images. Image semantics has shown to be useful to relieve the ill-posed nature of the SR problem, however the most accepted clustering-based approach used to define semantic concepts limits the capability of representing complex visual relationships. The proposed approach provides a new probabilistic perspective where the SR process is performed according to the semantics encapsulated by a new topic model, the Sparse Multimodal probabilistic Latent Semantic Analysis (sMpLSA). Firstly, the sMpLSA model is formulated. Subsequently, a new SR framework based on sMpLSA is defined. Finally, an experimental comparison is conducted using seven learningbased SR methods over three different image datasets. Experiments reveal the potential of latent topics in SR by reporting that the proposed approach is able to provide a competitive performance.

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“…Zhang and Zhong collected large-scale external data to build the topic model according to the LDA, which enables word topics to enrich feature representations of short text [22]. Vo and Ock employed a LDA-based method to discover hidden topic from universal datasets (e.g., Computer Science Bibliography), Lecture Notes in Computer Science book series (LNCS), and Wikipedia [23].…”

Section: Related Workmentioning

confidence: 99%

A Novel Mobile Video Community Discovery Scheme Using Ontology-Based Semantical Interest Capture

Zhang

Xiong

2016

Mobile Information Systems

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Leveraging network virtualization technologies, the community-based video systems rely on the measurement of common interests to define and steady relationship between community members, which promotes video sharing performance and improves scalability community structure. In this paper, we propose a novel mobile Video Community discovery scheme using ontologybased semantical interest capture (VCOSI). An ontology-based semantical extension approach is proposed, which describes video content and measures video similarity according to video key word selection methods. In order to reduce the calculation load of video similarity, VCOSI designs a prefix-filtering-based estimation algorithm to decrease energy consumption of mobile nodes. VCOSI further proposes a member relationship estimate method to construct scalable and resilient node communities, which promotes video sharing capacity of video systems with the flexible and economic community maintenance. Extensive tests show how VCOSI obtains better performance results in comparison with other state-of-the-art solutions.

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Learning to classify short text from scientific documents using topic models with various types of knowledge

Cited by 66 publications

References 21 publications

Interactive generic learning method (IGLM)

Interactive generic learning method (IGLM)

Sparse multi-modal probabilistic latent semantic analysis for single-image super-resolution

A Novel Mobile Video Community Discovery Scheme Using Ontology-Based Semantical Interest Capture

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