A Siamese Neural Network for Learning Semantically-Informed Sentence Embeddings

Bölücü, Necva; Can, Burcu; Artuner, Harun

doi:10.1016/j.eswa.2022.119103

Cited by 11 publications

(6 citation statements)

References 110 publications

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“…Consistency scores were calculated as part of the sentiment analysis, as shown in Table 1. Semantic similarity is a value between 0% and 100%, where a higher percentage indicates that the results were more consistent [33]. On average, the transcripts coded using the sentiment analysis model were more consistent than those coded using the manual approach.…”

Section: Consistencymentioning

confidence: 99%

Comparing text mining and manual coding methods: Analysing interview data on quality of care in long-term care for older adults

Hacking,

Verbeek,

Hamers

et al. 2023

PLoS ONE

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Objectives In long-term care for older adults, large amounts of text are collected relating to the quality of care, such as transcribed interviews. Researchers currently analyze textual data manually to gain insights, which is a time-consuming process. Text mining could provide a solution, as this methodology can be used to analyze large amounts of text automatically. This study aims to compare text mining to manual coding with regard to sentiment analysis and thematic content analysis. Methods Data were collected from interviews with residents (n = 21), family members (n = 20), and care professionals (n = 20). Text mining models were developed and compared to the manual approach. The results of the manual and text mining approaches were evaluated based on three criteria: accuracy, consistency, and expert feedback. Accuracy assessed the similarity between the two approaches, while consistency determined whether each individual approach found the same themes in similar text segments. Expert feedback served as a representation of the perceived correctness of the text mining approach. Results An accuracy analysis revealed that more than 80% of the text segments were assigned the same themes and sentiment using both text mining and manual approaches. Interviews coded with text mining demonstrated higher consistency compared to those coded manually. Expert feedback identified certain limitations in both the text mining and manual approaches. Conclusions and implications While these analyses highlighted the current limitations of text mining, they also exposed certain inconsistencies in manual analysis. This information suggests that text mining has the potential to be an effective and efficient tool for analysing large volumes of textual data in the context of long-term care for older adults.

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Section: Consistencymentioning

confidence: 99%

Comparing text mining and manual coding methods: Analysing interview data on quality of care in long-term care for older adults

Hacking,

Verbeek,

Hamers

et al. 2023

PLoS ONE

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“…Bölücü N., Can B., Artuner H. [8] discuss the peculiarities of sense representation as a way of expressing the meaning of a text that can be processed by a machine to perform a certain task in the context of natural language processing (NLP). In their work, the researchers present a model of semantic parsing based on artificial neural networks that helps to obtain a semantic representation of a given sentence.…”

Section: Neural Network Modeling As a Tool For Analyzing Language Unitsmentioning

confidence: 99%

Analysis of the Theoretical Foundations of Neural Network Modeling of Language Unit Recognition

Dovhan¹

2023

Scientific Space: Integration of Traditional and Innovative Processes

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In the context of linguistic science and the integration of the mathematical paradigm into humanitarian discourse, the analysis and processing of natural language (in particular, neural network modeling of language categories) is an important and urgent task. Research by a number of authors and a series of experiments show that artificial neural networks of various types and kinds with different parameterizations can significantly optimize linguistic research: accelerate, deepen, integrate into various scientific fields, etc. At the same time, the use of artificial neural networks in linguistics is an important area of work, as well as a powerful and productive tool for a number of relevant studies, which, however, requires careful analysis and development of implementation strategies. The purpose of the article is to analyze the features of neural network modeling of language units recognition as an effective method of cognition within the anthropocentric paradigm of research. The solution of such research tasks determines the logic of presentation of the studied material in the article: introduction, systematization of achievements in the theory and practice of modeling as a universal tool of scientific cognition in general and theoretical justification of neural network modeling in the context of linguistic paradigm. Methodology of the study is based of the method of analyzing scientific research was updated, which led to the search and analysis of scientific publications related to neural network modeling (in particular, language units). We analyzed more than 60 recent scientific studies and publications covering aspects of the problem under study, which we evaluated based on their relevance, methodological specificity, and scientific novelty. Thus, the research methods outlined in this article allowed us to conduct a thorough analysis of the state and prospects for the development of the theoretical foundations of neural network modeling of language unit recognition. The analysis of the latest scientific research and publications has made it possible to determine the role and place of artificial neural networks of various types and their specifications in the process of modeling language units. The above made it possible to identify the main trends in working with text data aimed at improving the quality of their processing, generation, etc. Results of the survey showed that one of the core tasks of modern linguistic science is to understand the language polysystem, the peculiarities of its structure and the nature of its functioning. In addition, the discursive nature of language practices in the context of data interconnection is also important for documenting language structures and verbal practices that are socially determined. That is why the use of neural networks as a tool for conducting local linguistic and integrated scientific research involving the mathematical paradigm is gaining popularity. Neural network modeling of language categories in this context is the basic basis for such research. Practical implications. The actualization of neural network modeling of linguistic categories is not only one of the means of studying linguistic polysystems, but also an objective criterion of checking the truth of linguistic knowledge. Value/originality. Neural network modeling of language unit recognition has a significant impact on the development, improvement, and evolution of modern linguistic research. The effectiveness of neural network modeling of language categories creates opportunities for a deeper understanding of the studied linguistic objects, phenomena and processes, encouraging linguists to develop various linguistic models that could solve practical linguistic problems (information retrieval, machine translation, natural language processing, knowledge extraction and localization from text, etc.).

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“…Our motivation for choosing Siamese-based approach is model efficiency through weight sharing, imbalance learning, and flexibility of loss function selection. Hence, Siamese-based models have been widely shown to achieve superior performance for semantic similarity tasks [3].…”

Section: Why Does Attentivebuglocator Work?mentioning

confidence: 99%

AttentiveBugLocator: A Bug Localization Model using Attention-based SemanticFeatures and Information Retrieval

Ahmad,

Yu,

Kholief

et al. 2023

Preprint

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In recent years, deep learning-based algorithms such as CNN, LSTM, and autoencoders have been proposed to rank suspicious buggy files. Meanwhile,representational learning has served to be the best approach to extract rich semantic features of bug reports and source code to reduce their lexical mismatch. In this paper, we propose AttentiveBugLocator, a Siamese-based representational learning modelfor improved bug localization performance. AttentiveBugLocator employs BERT and code2vec embedding models to produce richer semantic representations and a Siamese BiLSTM network with context attention to learn semantic matching between BRs and SFs. To further improve the effectiveness of AttentiveBugLocator, the semantic matching features are carefully fused with VSM, stack trace, and code complexity features. Evaluation results on four data sets show that AttentiveBugLocator can identify buggy files on the scale of 56% and 62% on MAP and MRR – thus, outperforms several state-of-the-art approaches.

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A Siamese Neural Network for Learning Semantically-Informed Sentence Embeddings

Cited by 11 publications

References 110 publications

Comparing text mining and manual coding methods: Analysing interview data on quality of care in long-term care for older adults

Comparing text mining and manual coding methods: Analysing interview data on quality of care in long-term care for older adults

Analysis of the Theoretical Foundations of Neural Network Modeling of Language Unit Recognition

AttentiveBugLocator: A Bug Localization Model using Attention-based SemanticFeatures and Information Retrieval

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