Automating Articulation: Applying Natural Language Processing to Post-Secondary Credit Transfer

Heppner, Andrew; Pawar, Atish; Kivi, Daniel; Mago, Vijay

doi:10.1109/access.2019.2910145

Cited by 16 publications

(8 citation statements)

References 16 publications

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“…Natural Language Processing (NLP) is an important field for realizing effective communication between humans and computers [40]. Among the various tasks in NLP, text matching is a fundamental task, and it has been used in a wide range of applications, such as information retrieval, question-answering, and machine translation [41].…”

Section: B Text Matchingmentioning

confidence: 99%

Combination of ELMo Representation and CNN Approaches to Enhance Service Discovery

Huang

Zhao

2020

IEEE Access

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With the rapid growth of Web services, the demand for discovering the optimal services to satisfy the users' requirements is no longer an easy task. The critical issue in the process of service discovery is to conduct a similarity calculation. To solve such an issue, this study proposes an effective approach that combines the Embeddings from Language Models (ELMo) representation and Convolutional Neural Network (CNN) to obtain a more accurate similarity score for retrieving target Web services. More specifically, first, the study adopts the ELMo model to generate effective word representations for capturing the sufficient information from services and queries. Then, the word representations are used to compose a similarity matrix, which will be taken as the input for the CNN to learn the matching relationships. Finally, the combination of the ELMo representation and CNN is used to address the representation and interaction processes within the matching task to improve the service discovery performance. The results demonstrate the effectiveness of our proposed approach for retrieving better targeted Web services.

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Section: B Text Matchingmentioning

confidence: 99%

Combination of ELMo Representation and CNN Approaches to Enhance Service Discovery

Huang

Zhao

2020

IEEE Access

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“…In existing system, a table-based generative model has been presented by. Heppner et al [11] has introduced a partial automated natural language processing for investigation specific traits over a domain. A unique approach to draw inference has been presented by Huang et al [12] where a semantic-based network system has been constructed for correlating text with an image.…”

Section: A Backgroundmentioning

confidence: 99%

Modelling Simple and Efficient Data Transformation Scheme for Improving Natural Language Processing

J*,

Swamy

2020

IJITEE

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The importance of natural language processing cannot be sided in the current era of communication and analytics where data is exponentially growing. Although, there has been various versions and schemes that has evolved in the past decade towards improving the performance of natural language processing, but still the problem towards precisely extracting the actual context is an open ended. Review of existing studies show a large scope of new work towards improving it. Therefore, this manuscript presents a unique simplified approach where natural language processing is carried out with a combined effort of syntactical and semantic based transformation scheme. The study is implemented using analytical methodology while the secondary motive of the work is also to balance the mining performance as well as optimizing storage performance too. The study outcome shows proposed scheme to excel better performance with respect to time duration in all the internal processes being involved for data transformation.

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“…Analyzing the readability of the sentences in these datasets, we find that the sentences in these datasets have a low readability index which is a measure of complexity of sentences [17]. However, various real world applications of semantic similarity involve more complex sentences to be analysed [18]. In this paper, a new dataset with sentences of a higher degree of complexity than the existing datasets is proposed.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Word Embeddings in Assessing Semantic Similarity of Complex Sentences

Chandrasekaran

Mago²

2021

IEEE Access

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Semantic textual similarity is one of the open research challenges in the field of Natural Language Processing. Extensive research has been carried out in this field and near-perfect results are achieved by recent transformer-based models in existing benchmark datasets like the STS dataset and the SICK dataset. In this paper, we study the sentences in these datasets and analyze the sensitivity of various word embeddings with respect to the complexity of the sentences. In this article, we build a complex sentence dataset comprising of 50 sentence pairs with associated semantic similarity values provided by 15 human annotators. Readability analysis is performed to highlight the increase in complexity of the sentences in the existing benchmark datasets and those in the proposed dataset. Further, we perform a comparative analysis of the performance of various word embeddings and language models on the existing benchmark datasets and the proposed dataset. The results show the increase in complexity of the sentences has a significant impact on the performance of the embedding models resulting in a 10-20% decrease in Pearson's and Spearman's correlation.

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Automating Articulation: Applying Natural Language Processing to Post-Secondary Credit Transfer

Cited by 16 publications

References 16 publications

Combination of ELMo Representation and CNN Approaches to Enhance Service Discovery

Combination of ELMo Representation and CNN Approaches to Enhance Service Discovery

Modelling Simple and Efficient Data Transformation Scheme for Improving Natural Language Processing

Comparative Analysis of Word Embeddings in Assessing Semantic Similarity of Complex Sentences

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