NLP-based Automatic Answer Evaluation

Sinha, Shubham Kumar; Yadav, Sachin; Verma, Bindu

doi:10.1109/iccmc53470.2022.9754052

Cited by 10 publications

(4 citation statements)

References 11 publications

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“…The authors emphasize the interconnectedness of inputs, outputs, and tasks in these models, prompting critical inquiry into the definition and scope of VLMs. This study lays the groundwork for comprehending the complexities involved in merging visual and textual modalities within AI systems [2]. Similarly, the advent of Large Language Models (LLMs) has transformed the landscape of natural language comprehension.…”

Section: Literature Surveymentioning

confidence: 99%

Co-Vision: Automated Video Context Understanding and Classification Using AI

et al. 2024

IJFMR

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In today's digital landscape, a challenge is presented by the ever-growing volume of video content: how to efficiently understand, access, and utilize this wealth of information. This challenge is addressed through developing an innovative web application combining video understanding, content recommendation, and accessibility features. The web application's core is found in its ability to be automatically analyzed and comprehended by videos' content. Valuable insights are extracted, key events, objects, and actions are identified, and textual explanations are generated, all of which are leveraged by state-of-the-art machine learning techniques in computer vision and natural language processing. The comprehension of video content forms the foundation for two significant applications, such as content recommendation, in which the content of videos and user preferences are understood, and our application revolutionizes content recommendation systems. Personalized video recommendations are provided, ensuring that content is discovered by users that precisely match their interests and needs. This user experience enhancement, along with resulting higher user engagement and content consumption on recommendation-driven platforms, is achieved. Accessibility is increased as the project promotes inclusivity by making video content more accessible to a broader audience. For individuals with disabilities, textual explanations and transcripts are generated by our application, breaking down barriers to understanding. Additionally, the convenience of summarizing lengthy videos is offered, enabling users to quickly grasp key insights without the need to watch the entire content. This enhanced accessibility is particularly valuable in educational contexts and beyond, where content consumption is made more efficient and equitable.

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Section: Literature Surveymentioning

confidence: 99%

Co-Vision: Automated Video Context Understanding and Classification Using AI

et al. 2024

IJFMR

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“…In Paper [6], it primarily centers on the utilization of NLP techniques, specifically NLTK and Ngrams, for assessing text similarity. It serves as a guide for conducting text similarity analysis on provided input.…”

Section: Literature Surveymentioning

confidence: 99%

IntelliView: An AI Based Mock Interview Platform

Journal

2024

IJSREM

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The IntelliView initiative represents a pioneering endeavor designed to empower novice job seekers through the integration of state-of-the-art Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies. This platform employs a sophisticated blend of HTML, CSS, JavaScript, and the Deep Face method, introducing a comprehensive framework to redefine interview assessment and augment job application preparation. The primary module facilitates text-based analysis, enabling users to engage with real-time interview questions by entering responses directly into the web interface. Subsequently, advanced algorithms compare users' responses to expected answers, yielding a percentage similarity score and presenting the correct answer. Beyond affording essential interview practice, this feature furnishes constructive feedback, enhancing users' responses and ultimately refining their interview performance. In the second module, IntelliView introduces a dynamic interview environment with video-based analysis. Users respond to inquiries utilizing webcams, enabling the system to meticulously record both verbal responses and facial expressions. Leveraging the Deep Face method, the platform conducts real-time emotion and sentiment analysis, offering users insights into their emotional states throughout interviews. This feedback facilitates the refinement of non-verbal communication skills, empowering candidates to recognize emotional tendencies and adapt interview strategies accordingly. The third module functions as a comprehensive resume builder, employing HTML, CSS, and JavaScript to provide diverse templates tailored to individual needs. In summation, IntelliView heralds a transformative paradigm in job application preparation, seamlessly amalgamating technological advancements and human interaction to equip first-time job seekers with the requisite tools for navigating the competitive job market successfully. Key Words: Interview Assessment, DeepFace, NLP, Text- based analysis, Video-based analysis, Resume Builder

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“…Many folks sought to plug into the competition platform framework, to use its infrastructure, load the data, install well-known libraries, and start their particular investigations. A number of participants discussed diffculties they experienced when getting started with the dataset [13][14][15][16][17][18][19][20][21][22]. Some of these were also discussed as the following kinds of topics: issues with data size being too large [23], corrupted data fles [24], duplicate entries [25,26], confusion about the data [27], parsing errors in the JSON data [28], conversion of JSON data [11], and more.…”

Section: Mechanicsmentioning

confidence: 99%

An infrastructure for curating, querying, and augmenting document data :

Subrahmanian¹

2023

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With the advent of the COVID-19 pandemic, there was the hope that data science approaches could help discover means for understanding, mitigating, and treating the disease. This manifested itself in the creation of the COVID-19 Open Research Dataset (CORD-19) which aggregated COVID-19- related scientific literature for use by the data mining community. As a group of interdisciplinary informatics researchers at NIST, we embarked on an effort to use our experience and previously developed systems to explore whether we could enhance the CORD-19 data set and facilitate its use. This effort produced a prototype scientific informatics system that extended data curation, data repository, resource registry, term extraction and indexing systems and resulted in a repackaging of CORD-19 as a Python data package. This paper documents our efforts, provides lessons learned, and proposes a general architecture for these types of systems.

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NLP-based Automatic Answer Evaluation

Cited by 10 publications

References 11 publications

Co-Vision: Automated Video Context Understanding and Classification Using AI

Co-Vision: Automated Video Context Understanding and Classification Using AI

IntelliView: An AI Based Mock Interview Platform

An infrastructure for curating, querying, and augmenting document data :

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