A survey on textual entailment based question answering

Paramasivam, Aarthi; Nirmala, S. Jaya

doi:10.1016/j.jksuci.2021.11.017

Cited by 12 publications

(3 citation statements)

References 16 publications

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“…The numbers of questionanswer pairs and corresponding images are listed below: 12, and the x-axis represents the interval of word frequency. The word frequency of the questions is concentrated in the interval [5,8], and the word frequency of the answers is concentrated in the interval [1,4]. We set the maximum sentence length to 11.…”

Section: Imageclef2019 Vqa-medmentioning

confidence: 99%

“…The representative works are the MedQA [1], MiPACQ [2], and AskHERMES [3] systems. Current medical QA systems are generally based on knowledge mapping technology, which stores medical information in the form of an entity-relationship in a non-relational database, and they provide medical advice by searching and reasoning, Aarthi [4] enumerates the traditional subtasks of QA, including almost all MedQA questions. For example, Izcovich [5] developed a GRADE-based medical question answering system.…”

Section: Introductionmentioning

confidence: 99%

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BPI-MVQA: a bi-branch model for medical visual question answering

et al. 2022

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Background Visual question answering in medical domain (VQA-Med) exhibits great potential for enhancing confidence in diagnosing diseases and helping patients better understand their medical conditions. One of the challenges in VQA-Med is how to better understand and combine the semantic features of medical images (e.g., X-rays, Magnetic Resonance Imaging(MRI)) and answer the corresponding questions accurately in unlabeled medical datasets. Method We propose a novel Bi-branched model based on Parallel networks and Image retrieval for Medical Visual Question Answering (BPI-MVQA). The first branch of BPI-MVQA is a transformer structure based on a parallel network to achieve complementary advantages in image sequence feature and spatial feature extraction, and multi-modal features are implicitly fused by using the multi-head self-attention mechanism. The second branch is retrieving the similarity of image features generated by the VGG16 network to obtain similar text descriptions as labels. Result The BPI-MVQA model achieves state-of-the-art results on three VQA-Med datasets, and the main metric scores exceed the best results so far by 0.2$$\%$$ % , 1.4$$\%$$ % , and 1.1$$\%$$ % . Conclusion The evaluation results support the effectiveness of the BPI-MVQA model in VQA-Med. The design of the bi-branch structure helps the model answer different types of visual questions. The parallel network allows for multi-angle image feature extraction, a unique feature extraction method that helps the model better understand the semantic information of the image and achieve greater accuracy in the multi-classification of VQA-Med. In addition, image retrieval helps the model answer irregular, open-ended type questions from the perspective of understanding the information provided by images. The comparison of our method with state-of-the-art methods on three datasets also shows that our method can bring substantial improvement to the VQA-Med system.

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Section: Imageclef2019 Vqa-medmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

BPI-MVQA: a bi-branch model for medical visual question answering

et al. 2022

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“…However, text processing of large corpora is time-consuming, resource-intensive, and thus costly. In addition, NLP routines are required as pre-processing steps to perform even more time-consuming tasks as, e.g., textual entailment (Paramasivam and Nirmala, 2021), rhetorical analysis (Joty et al, 2015) or argument mining (Ding et al, 2022). In any event, the application of NLP methods is gaining acceptance in almost all text-based disciplines.…”

Section: Introductionmentioning

confidence: 99%

Unlocking the Heterogeneous Landscape of Big Data NLP with DUUI

Leonhardt,

Abrami,

Baumartz

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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Automatic analysis of large corpora is a complex task, especially in terms of time efficiency. This complexity is increased by the fact that flexible, extensible text analysis requires the continuous integration of ever new tools. Since there are no adequate frameworks for these purposes in the field of NLP, and especially in the context of UIMA, that are not outdated or unusable for security reasons, we present a new approach to address the latter task: DOCK-ER UNIFIED UIMA INTERFACE (DUUI), a scalable, flexible, lightweight, and feature-rich framework for automatic distributed analysis of text corpora that leverages Big Data experience and virtualization with Docker. We evaluate DUUI's communication approach against a state-of-the-art approach and demonstrate its outstanding behavior in terms of time efficiency, enabling the analysis of big text data.

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