A Rule-Based Approach to Embedding Techniques for Text Document Classification

Aubaid, Asmaa M.; Mishra, Alok

doi:10.3390/app10114009

Cited by 34 publications

(12 citation statements)

References 31 publications

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“…6 and Fig. 15 defines a detailed comparative results analysis of the CIDD-ADODNN model on the test Chess dataset [26]. The resultant values addressed that ZeroR and SVM models have depicted poor performance by accomplishing minimal accuracy values of 0.390 and From the detailed experimental analysis, it is evident that the CIDD-ADODNN model has accomplished an effective outcome on all the applied dataset.…”

Section: Performance Validationmentioning

confidence: 90%

Deep learning framework for handling concept drift and class imbalanced complex decision-making on streaming data

Priya

Uthra

2021

Complex Intell. Syst.

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In present times, data science become popular to support and improve decision-making process. Due to the accessibility of a wide application perspective of data streaming, class imbalance and concept drifting become crucial learning problems. The advent of deep learning (DL) models finds useful for the classification of concept drift in data streaming applications. This paper presents an effective class imbalance with concept drift detection (CIDD) using Adadelta optimizer-based deep neural networks (ADODNN), named CIDD-ADODNN model for the classification of highly imbalanced streaming data. The presented model involves four processes namely preprocessing, class imbalance handling, concept drift detection, and classification. The proposed model uses adaptive synthetic (ADASYN) technique for handling class imbalance data, which utilizes a weighted distribution for diverse minority class examples based on the level of difficulty in learning. Next, a drift detection technique called adaptive sliding window (ADWIN) is employed to detect the existence of the concept drift. Besides, ADODNN model is utilized for the classification processes. For increasing the classifier performance of the DNN model, ADO-based hyperparameter tuning process takes place to determine the optimal parameters of the DNN model. The performance of the presented model is evaluated using three streaming datasets namely intrusion detection (NSL KDDCup) dataset, Spam dataset, and Chess dataset. A detailed comparative results analysis takes place and the simulation results verified the superior performance of the presented model by obtaining a maximum accuracy of 0.9592, 0.9320, and 0.7646 on the applied KDDCup, Spam, and Chess dataset, respectively.

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Section: Performance Validationmentioning

confidence: 90%

Deep learning framework for handling concept drift and class imbalanced complex decision-making on streaming data

Priya

Uthra

2021

Complex Intell. Syst.

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“…Word embeddings are unsupervised learning applications that also talk about transfer learning as it is incorporated in the given user corpus. Embeddings can be character level or word level [14]. The word level embeddings use word2vec method where the basic construct of embeddings is converting words into vectors and then mathematically apply relations on them based on the corpus being used.…”

Section: A Word Embeddingsmentioning

confidence: 99%

Query Expansion based on Word Embeddings and Ontologies for Efficient Information Retrieval

Rastogi¹,

Verma²,

Kumar³

2021

IJACSA

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Information retrieval has been an ever-going process for end users to fetch relevant data at one go. The problem intensifies more with unstructured data in a semantic web environment. It is also a promising area for researchers to dive in and refine it from time to time. Expanding the user query and reformulating it is one probable solution to increase the efficiency of the information retrieval system. In this paper we propose "WeOnto", a novel two-level query expansion algorithm that utilizes the combination of web ontologies and word embeddings for similarity calculation. In the first level, the Real estate Ontology (REO) is created using Protégé and Sparql queries are passed to retrieve probable semantic words from the given ontology for each inputted user query. The first level gave significant results and improved the information retrieval by 18%. The second level of algorithm uses word embedding enhanced with the domain knowledge that helps to retrieve similar meaningful words based on cosine similarity for the same user query. Word embeddings are implemented using Word2Vec method that follows two architectures namely CBOW or Skip Gram. Most similar semantic words are retrieved using the CBOW word embeddings method in the proposed algorithm and concatenated with the semantic keywords generated from the real estate ontology to form a powerful reformulated query that gives promising relevant results. Finally, two topmost words as per their similarity index are taken to reformulate the original user query. Experimental results depict that proposed algorithm has given distinct results and has showcased significant improvement of 93% over the initial user query.

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“…In [30], Ronran et al evaluated the combination of different types of embedding features in a bidirectional long short-term memory (Bi-LSTM) conditional random field (CRF) model for named entity recognition (NER). The authors in [31] [32] proposed a rule-based approach for text document classification. The study in [33] proposed an RE model based on a dual pointer network with a multi-head attention mechanism to address the association of multiple entities in a sentence according to various relations.…”

Section: Document Analysis Information Extraction and Text Miningmentioning

confidence: 99%

Special Issue on “Natural Language Processing: Emerging Neural Approaches and Applications”

et al. 2021

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A Rule-Based Approach to Embedding Techniques for Text Document Classification

Cited by 34 publications

References 31 publications

Deep learning framework for handling concept drift and class imbalanced complex decision-making on streaming data

Deep learning framework for handling concept drift and class imbalanced complex decision-making on streaming data

Query Expansion based on Word Embeddings and Ontologies for Efficient Information Retrieval

Special Issue on “Natural Language Processing: Emerging Neural Approaches and Applications”

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