Domain Based Ontology and Automated Text Categorization Based on Improved Term Frequency – Inverse Document Frequency

Ray, Sukanya; Chandra, Nidhi

doi:10.5815/ijmecs.2012.04.04

Cited by 7 publications

(5 citation statements)

References 4 publications

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“…For semantic detection, the candidate semantic representation should be obtained first, there are some state-of-art methods such as keyword-based representation [29] and semantic-based representation [30]. Our ground-truth labels are some short sentences, and those sentences of a video might be composed of different words.…”

Section: Semantic Reconstruction Networkmentioning

confidence: 99%

“…To avoid the influence of semantic represents methods, we built the semantic representation tags by the manual selection which are K most common words in the training set. Manual selection is not feasible if the dataset is extremely large, then the semantic representation tags should build by semantic represent methods such as TF-IDF [29]. The task of semantic detection can be seen as a multi-label classification task.…”

Section: Semantic Reconstruction Networkmentioning

confidence: 99%

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Video Captioning Based on Channel Soft Attention and Semantic Reconstructor

Zhou

Huang

2021

Future Internet

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Video captioning is a popular task which automatically generates a natural-language sentence to describe video content. Previous video captioning works mainly use the encoder–decoder framework and exploit special techniques such as attention mechanisms to improve the quality of generated sentences. In addition, most attention mechanisms focus on global features and spatial features. However, global features are usually fully connected features. Recurrent convolution networks (RCNs) receive 3-dimensional features as input at each time step, but the temporal structure of each channel at each time step has been ignored, which provide temporal relation information of each channel. In this paper, a video captioning model based on channel soft attention and semantic reconstructor is proposed, which considers the global information for each channel. In a video feature map sequence, the same channel of every time step is generated by the same convolutional kernel. We selectively collect the features generated by each convolutional kernel and then input the weighted sum of each channel to RCN at each time step to encode video representation. Furthermore, a semantic reconstructor is proposed to rebuild semantic vectors to ensure the integrity of semantic information in the training process, which takes advantage of both forward (semantic to sentence) and backward (sentence to semantic) flows. Experimental results on popular datasets MSVD and MSR-VTT demonstrate the effectiveness and feasibility of our model.

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Section: Semantic Reconstruction Networkmentioning

confidence: 99%

Section: Semantic Reconstruction Networkmentioning

confidence: 99%

Video Captioning Based on Channel Soft Attention and Semantic Reconstructor

Zhou

Huang

2021

Future Internet

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“…Based on these representations, they compute multiple clustering results using KMeans. Ray and Chandra [4] proposed an automated text categorization technique that will categorize the uncategorized documents. The idea was based on the Term Frequency -Inverse Document Frequency (tf-idf) method and a dependency graph is also provided in the domain based ontology so that the users can visualize the relations among the terms.…”

Section: Introductionmentioning

confidence: 99%

Advancing the Terminological Classification of Semi-structured Documents

Stratogiannis

Siolas

Stamou

et al. 2015

2015 IEEE 27th International Conference on Tools With Artificial Intelligence (ICTAI)

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Usually, documents are given in textual form, accompanied by a set of terminological classifications (metadata), based on vocabularies of domain ontologies. This paper presents a novel method for advancing the above classification, by extracting more properties of the analyzed documents. We first extract additional roles from the textual part and together with roles extracted from the ontology statements, we construct an extended document vector representation. We then introduce a pruning algorithm that, for a given document collection, merges concepts of the ontology to produce classes with a sufficient number of corresponding instances. We then classify the documents to ontology classes using the Stanford linear Classifier. Finally, we propose an algorithm that assigns additional concept labels to documents, using the output of the classifier. Our system is evaluated in a set of real data and ontological descriptions and its performance is measured in terms of various accuracy and specificity measures indicates that the proposed approach for documents classification produces correct labels for the majority of items.

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“…Furthermore, the shallow visual feature is extracted based on the statistical pixel values of each category block. In addition, Term Frequency -Inverse Document Frequency (TF-IDF) [20] is also applied as a weighting method to reinforce the distinction between text format category blocks.…”

Section: Introductionmentioning

confidence: 99%

VTLayout: Fusion of Visual and Text Features for Document Layout Analysis

Li¹,

Ma²,

Pan³

et al. 2021

Preprint

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Documents often contain complex physical structures, which make the Document Layout Analysis (DLA) task challenging. As a preprocessing step for content extraction, DLA has the potential to capture rich information in historical or scientific documents on a large scale. Although many deep-learning-based methods from computer vision have already achieved excellent performance in detecting Figure from documents, they are still unsatisfactory in recognizing the List, Table , Text and Title category blocks in DLA. This paper proposes a VTLayout model fusing the documents' deep visual, shallow visual, and text features to localize and identify different category blocks. The model mainly includes two stages, and the three feature extractors are built in the second stage. In the first stage, the Cascade Mask R-CNN model is applied directly to localize all category blocks of the documents. In the second stage, the deep visual, shallow visual, and text features are extracted for fusion to identify the category blocks of documents. As a result, we strengthen the classification power of different category blocks based on the existing localization technique. The experimental results show that the identification capability of the VTLayout is superior to the most advanced method of DLA based on the PubLayNet dataset, and the F1 score is as high as 0.9599.

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Domain Based Ontology and Automated Text Categorization Based on Improved Term Frequency – Inverse Document Frequency

Cited by 7 publications

References 4 publications

Video Captioning Based on Channel Soft Attention and Semantic Reconstructor

Video Captioning Based on Channel Soft Attention and Semantic Reconstructor

Advancing the Terminological Classification of Semi-structured Documents

VTLayout: Fusion of Visual and Text Features for Document Layout Analysis

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