Fusing Document, Collection and Label Graph-based Representations
            with Word Embeddings for Text Classification

Skianis, Konstantinos; Malliaros, Fragkiskos D.; Vazirgiannis, Michalis

doi:10.18653/v1/w18-1707

Cited by 17 publications

(11 citation statements)

References 33 publications

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“…Two data representation models (TF-IDF and Word2vec) and datasets (20 newsgroups and WebKB) are studied in this work [54]. 20 newsgroup dataset with TF-IDF representation exhibits 83.0% classification accuracy while the Word2vec version of the same dataset presents 75.8% classification success in [54]. The TF-IDF representation model of our study performs 91.5% and the Word2vec representation of our work achieves 88.5% classification accuracies for 20 newsgroup dataset.…”

Section: Experiments Resultsmentioning

confidence: 84%

“…The TF-IDF representation model of our study performs 91.5% and the Word2vec representation of our work achieves 88.5% classification accuracies for 20 newsgroup dataset. Moreover, their study [54] with WebKB dataset and TF-IDF representation exhibits 89.9% classification accuracy while the Word2vec version of the same dataset presents 86.6% classification success. The TF-IDF representation model of our work performs 91.2%, and the Word2vec representation of ours represents 91.7% classification accuracies for WebKB dataset.…”

Section: Experiments Resultsmentioning

confidence: 97%

Section: Experiments Resultsmentioning

confidence: 99%

“…In another study [54], a graph-based framework is presented by utilizing SVM for text classification problem. Two data representation models (TF-IDF and Word2vec) and datasets (20 newsgroups and WebKB) are studied in this work [54]. 20 newsgroup dataset with TF-IDF representation exhibits 83.0% classification accuracy while the Word2vec version of the same dataset presents 75.8% classification success in [54].…”

Section: Experiments Resultsmentioning

confidence: 99%

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Deep Learning‐ and Word Embedding‐Based Heterogeneous Classifier Ensembles for Text Classification

Kilimci

Akyokuş

2018

Complexity

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The use of ensemble learning, deep learning, and effective document representation methods is currently some of the most common trends to improve the overall accuracy of a text classification/categorization system. Ensemble learning is an approach to raise the overall accuracy of a classification system by utilizing multiple classifiers. Deep learning-based methods provide better results in many applications when compared with the other conventional machine learning algorithms. Word embeddings enable representation of words learned from a corpus as vectors that provide a mapping of words with similar meaning to have similar representation. In this study, we use different document representations with the benefit of word embeddings and an ensemble of base classifiers for text classification. The ensemble of base classifiers includes traditional machine learning algorithms such as naïve Bayes, support vector machine, and random forest and a deep learning-based conventional network classifier. We analysed the classification accuracy of different document representations by employing an ensemble of classifiers on eight different datasets. Experimental results demonstrate that the usage of heterogeneous ensembles together with deep learning methods and word embeddings enhances the classification performance of texts.

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Section: Experiments Resultsmentioning

confidence: 84%

Section: Experiments Resultsmentioning

confidence: 97%

Section: Experiments Resultsmentioning

confidence: 99%

Section: Experiments Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning‐ and Word Embedding‐Based Heterogeneous Classifier Ensembles for Text Classification

Kilimci

Akyokuş

2018

Complexity

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“…Graph-based methods can also be used for document relation structures [22] [23]. A microblog forwarding relationship is a type of document relation network that can provide much additional information [24].…”

Section: Introductionmentioning

confidence: 99%

A Novel Sentence Embedding Based Topic Detection Method for Microblogs

2020

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Topic detection is a difficult challenging task, especially when the exact number of topics is unknown. In this paper, we present a novel topic detection approach based on neural computing to detect topics in a microblogging dataset. We use an unsupervised neural sentence embedding model to map blogs to an embedding space. The proposed model is a weighted power mean sentence embedding model in which weights are calculated by a targeted attention mechanism. The experimental results show that our embedding model performs better than baseline in sentence clustering. In addition, we propose a clustering algorithm, referred to as Relationship-Aware DBSCAN (RADBSCAN), to discover topics from a microblogging dataset in which the number of topics is automatically determined by the characteristics of the dataset. Moreover, to provide parameter insensibility, we use the forwarding relationship in the blogs as a bridge of two independent clusters. Finally, we validate the proposed method on a dataset from the Sina microblog. The results show that our approach can detect all topics successfully and can extract the keywords of each topic.

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Using PMI to Rank and Filter Edges in Graphs of Words

Oliveira

Spinosa

2021

Artificial Intelligence and Soft Computing

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Fusing Document, Collection and Label Graph-based Representations with Word Embeddings for Text Classification

Cited by 17 publications

References 33 publications

Deep Learning‐ and Word Embedding‐Based Heterogeneous Classifier Ensembles for Text Classification

Deep Learning‐ and Word Embedding‐Based Heterogeneous Classifier Ensembles for Text Classification

A Novel Sentence Embedding Based Topic Detection Method for Microblogs

Using PMI to Rank and Filter Edges in Graphs of Words

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