Answer To Question Research Articles

AI Driven Chatbot Counsellor

Speech and written information are fundamental to human communication. As a result, the majority of spoken and textual communication takes place on digital platforms like Twitter, Facebook, and WhatsApp, among others. Our model employs dual recurrent neural networks (RNNs) to encode the information from text and audio sequences, as spoken language and sound constitute emotional discourse. The emotion class is then predicted by combining the data from the two sources. Due to the complexity of speech emotion recognition, models that use audio properties to generate powerful classifiers have become increasingly important. Completing sentences or crafting sentences from a given starting word is a major aspect of natural language processing. In a way, it shows if a machine is capable of human creativity and mental processes. In order to assist handle diverse phrase generation challenges, we use natural language processing to train the machine for certain tasks. This is especially useful for application situations such as machine translation, automatic question answering, and summary creation. At the moment, OpenAI GPT and BERT are the most widely used language models for text generation and prediction. The approaches based on handwritten instructions, patterns, or statistical methods have been quickly superseded by the latest developments in deep learning and artificial intelligence, such as end-to-end trainable neural networks. This research presents a novel approach to deep neural learning-based chatbot creation. This approach builds a multilayer neural network to analyse and learn from the data. Furthermore, we employ supplementary limitations on the generation model to generate the correct response, which is capable of discerning the context of the discussion, the user's mood, and the anticipated response. This enables us to provide customised counselling replies depending on customer feedback. Through this study, two new corpora will be used to train the OpenAI GPT model, which will then be used to generate articles and long sentences. Finally, a comparison study will be carried out. Concurrently, we will use the BERT model to complete the task of context-based intermediate word prediction. Keywords : Artificial Intelligence, Data Science, NLP, Deep Learning, Machine Learning, GPT, Generative AI, Speech Synthesis

Disentangled Retrieval and Reasoning for Implicit Question Answering.

To date, most of the existing open-domain question answering (QA) methods focus on explicit questions where the reasoning steps are mentioned explicitly in the question. In this article, we study implicit QA where the reasoning steps are not evident in the question. Implicit QA is challenging in two aspects. First, evidence retrieval is difficult since there is little overlap between a question and its required evidence. Second, answer inference is difficult since the reasoning strategy is latent in the question. To tackle implicit QA, we propose a systematic solution denoted as DisentangledQA, which disentangles topic, attribute, and reasoning strategy from the implicit question to guide the retrieval and reasoning. Specifically, we disentangle the topic and attribute information from the implicit question to guide evidence retrieval. For answer reasoning, we propose a disentangled reasoning model for answer prediction based on retrieved evidence as well as the latent representation of the reasoning strategy. The disentangled framework empowers each module to focus on a specific latent element in the question, and thus, leads to effective representation learning for them. Experiments on the StrategyQA dataset demonstrate the effectiveness of our method in answering implicit questions, improving performance in evidence retrieval and answering inference by 31.7% and 4.5%, respectively, and achieving the best performance on the official leaderboard. In addition, our method achieved the best performance on the challenging EntityQuestions dataset, indicating the effectiveness in improving general open-domain QA tasks.

Evaluating LLMs on document-based QA: Exact answer selection and numerical extraction using CogTale dataset

Document-based Question-Answering (QA) tasks are crucial for precise information retrieval. While some existing work focus on evaluating large language model’s (LLMs) performance on retrieving and answering questions from documents, assessing the LLMs performance on QA types that require exact answer selection from predefined options and numerical extraction is yet to be fully assessed. In this paper, we specifically focus on this underexplored context and conduct empirical analysis of LLMs (GPT-4 and GPT-3.5) on question types, including single-choice, yes–no, multiple-choice, and number extraction questions from documents. We use the CogTale dataset for evaluation, which provide human expert-tagged responses, offering a robust benchmark for precision and factual grounding. We found that LLMs, particularly GPT-4, can precisely answer many single-choice and yes–no questions given relevant context, demonstrating their efficacy in information retrieval tasks. However, their performance diminishes when confronted with multiple-choice and number extraction formats, lowering the overall performance of the models on this task, indicating that these models may not yet be sufficiently reliable for the task. This limits the applications of LLMs on applications demanding precise information extraction and inference from documents, such as meta-analysis tasks. Our work offers a framework for ongoing dataset evaluation, ensuring that LLM applications for information retrieval and document analysis continue to meet evolving standards.

Bridging the Gap Between Information Seeking and Product Search Systems: Q&A Recommendation for E-Commerce

Consumers on a shopping mission often leverage both product search and information seeking systems, such as web search engines and Question Answering (QA) systems, in an iterative process to improve their understanding of available products and reach a purchase decision. While product search is useful for shoppers to find the actual products meeting their requirements in the catalog, information seeking systems can be utilized to answer any questions they may have to refine those requirements. The recent success of Large Language Models (LLMs) has opened up an opportunity to bridge the gap between the two tasks to help customers achieve their goals quickly and effectively by integrating conversational QA within product search. In this paper, we propose to recommend users Question-Answer (Q&A) pairs that are relevant to their product search and can help them make a purchase decision. We discuss the different aspects of the problem including the requirements and characteristics of the Q&A pairs, their generation, and the optimization of the Q&A recommendation task. We highlight the challenges, open problems, and suggested solutions to encourage future research in this emerging area.

Improve Question Classification Genetic Algorithm Based Feature Selection and Convolution Neural Network

Natural Language Processing (NLP) approaches play a crucial role in classifying inquiries and comprehending human language in diverse applications. A Question Answering System (QAS) consists of three components are question processing, information retrieval, and answer selection. Question Answering Systems (QASs) are a distinct form of information retrieval. The most crucial aspect of QAS is deciding on the question type since it influences the other sections following. However, an important question-answering system requires a prominent question classification system. In the past, there are different methods to solve this problem, such as rule-based learning, and hybrid approaches. However, the problem with these methods is that the rules require a lot of effort to create and are very limited. In this study, the utilization of genetic algorithm and deep neural network techniques enhances the quality control problem-solving process. This research utilizes the UIUC dataset. This collection comprises 5452 questions designed for learning purposes and an additional 500 questions specifically intended for assessment. The suggested solution involves converting each query into a matrix, with each row representing the Word2vec of a word. Subsequently, a Genetic Algorithm (GA) is employed to identify the most optimal features. Ultimately, a Convolutional Neural Network is utilized for classification, yielding a remarkable accuracy of 98.2% in our experimentation with the question dataset.

Question‐driven text summarization using an extractive‐abstractive framework

AbstractQuestion‐driven automatic text summarization is a popular technique to produce concise and informative answers to specific questions using a document collection. Both query‐based and question‐driven summarization may not produce reliable summaries nor contain relevant information if they do not take advantage of extractive and abstractive summarization mechanisms to improve performance. In this article, we propose a novel extractive and abstractive hybrid framework designed for question‐driven automatic text summarization. The framework consists of complimentary modules that work together to generate an effective summary: (1) discovering appropriate non‐redundant sentences as plausible answers using an open‐domain multi‐hop question answering system based on a convolutional neural network, multi‐head attention mechanism and reasoning process; and (2) a novel paraphrasing generative adversarial network model based on transformers rewrites the extracted sentences in an abstractive setup. Experiments show this framework results in more reliable abstractive summary than competing methods. We have performed extensive experiments on public datasets, and the results show our model can outperform many question‐driven and query‐based baseline methods (an R1, R2, RL increase of 6%–7% for over the next highest baseline).

Improving Topic Tracing with a Textual Reader for Conversational Knowledge Based Question Answering

Improving Topic Tracing with a Textual Reader for Conversational Knowledge Based Question Answering

Enhancing service excellence: analyzing natural language question answering with advanced cosine similarity

<span lang="EN-US">Information related to student services in higher education must be produced and disseminated in various forms. Covid-19 pandemic, student services with a remote model related to this question and answer become very important. To carry out this automation process, the advanced cosine similarity method is used to check the similarity of the questions to the database and statistics to calculate the similarity value of each word. The proposed paper proceeds with three phases. The first stage to solve this problem is the data processed in question; the professional next step is word insertion. It converts alphanumeric words to vector format. Each word is a vector that represents a point in space with a certain dimension. The recommended advanced cosine similarity data still must be analyzed into a statistical approach. We will measure accuracy to get results so that optimal results and answers are obtained, research procedures are carried out based on literature study, initial data collection and observation, system development, system testing, system analysis, and system evaluation. This research implemented in universities with student chat automation applications providing an accuracy 83.90% given by natural language question answering system (NLQAS) so that it can improve excellent service in universities.</span>

A multilingual semantic search chatbot framework

Chatbots are conversational agents which interact with users and simulate a human interaction. Companies use chatbots on their customer-facing sites to enhance user experience by answering questions about their products and directing users to relevant pages on the site. Existing Chatbots which are used for this purpose give responses based on pre-defined FAQs only. In this paper, we propose a framework for a chatbot which combines two approaches - retrieval from a knowledge base consisting of question answer pairs, combined with a natural language search mechanism which can scan through the paragraphs of text information. A feedback-based knowledge base update is implemented which provides continuous improvement in user experience. The framework achieves a result of 81.73 percent answer matching on SQuAD 1.1 and 69.21 percent answer matching on SQuAD 2.0. The framework also performs well on languages such as Spanish (67.32 percent answer match), Russian (61.43 percent answer match), Arabic (51.63 percent answer match) etc. by means of zero shot learning.

Cross-attention Based Text-image Transformer for Visual Question Answering

Background: Visual question answering (VQA) is a challenging task that requires multimodal reasoning and knowledge. The objective of VQA is to answer natural language questions based on corresponding present information in a given image. The challenge of VQA is to extract visual and textual features and pass them into a common space. However, the method faces the challenge of object detection being present in an image and finding the relationship between objects. Methods: In this study, we explored different methods of feature fusion for VQA, using pretrained models to encode the text and image features and then applying different attention mechanisms to fuse them. We evaluated our methods on the DAQUAR dataset. Results: We used three metrics to measure the performance of our methods: WUPS, Acc, and F1. We found that concatenating raw text and image features performs slightly better than selfattention for VQA. We also found that using text as query and image as key and value performs worse than other methods of cross-attention or self-attention for VQA because it might not capture the bidirectional interactions between the text and image modalities Conclusion: In this paper, we presented a comparative study of different feature fusion methods for VQA, using pre-trained models to encode the text and image features and then applying different attention mechanisms to fuse them. We showed that concatenating raw text and image features is a simple but effective method for VQA while using text as query and image as key and value is a suboptimal method for VQA. We also discussed the limitations and future directions of our work.

Deep-GenMut: Automated genetic mutation classification in oncology: A deep learning comparative study

Early cancer detection and treatment depend on the discovery of specific genes that cause cancer. The classification of genetic mutations was initially done manually. However, this process relies on pathologists and can be a time-consuming task. Therefore, to improve the precision of clinical interpretation, researchers have developed computational algorithms that leverage next-generation sequencing technologies for automated mutation analysis. This paper utilized four deep learning classification models with training collections of biomedical texts. These models comprise bidirectional encoder representations from transformers for Biomedical text mining (BioBERT), a specialized language model implemented for biological contexts. Impressive results in multiple tasks, including text classification, language inference, and question answering, can be obtained by simply adding an extra layer to the BioBERT model. Moreover, bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) have been leveraged to produce very good results in categorizing genetic mutations based on textual evidence. The dataset used in the work was created by Memorial Sloan Kettering Cancer Center (MSKCC), which contains several mutations. Furthermore, this dataset poses a major classification challenge in the Kaggle research prediction competitions. In carrying out the work, three challenges were identified: enormous text length, biased representation of the data, and repeated data instances. Based on the commonly used evaluation metrics, the experimental results show that the BioBERT model outperforms other models with an F1 score of 0.87 and 0.850 MCC, which can be considered as improved performance compared to similar results in the literature that have an F1 score of 0.70 achieved with the BERT model.

Advancing coordination in critical maritime infrastructure protection: Lessons from maritime piracy and cybersecurity

Critical maritime infrastructure protection has become a priority in ocean governance, particularly in Europe. Increased geopolitical tensions, regional conflicts, and the Nord Stream pipeline attacks in the Baltic Sea of September 2022 have been the main catalysts for this development. Calls for enhancing critical maritime infrastructure protection have multiplied, yet, what this implies in practice is less clear. This is partially a question of engineering and risk analysis. It also concerns how the multitude of actors involved can act concertedly. Dialogue, information sharing, and coordination are required, but there is a lack of discussion about which institutional set ups would lend themselves. In this article, we argue that the maritime counter-piracy operations off Somalia, as well as maritime cybersecurity governance hold valuable lessons to provide new answers for the institutional question in the critical maritime infrastructure protection agenda. We start by clarifying what is at stake in the CMIP agenda and why it is a major contemporary governance challenge. We then examine and assess the instruments found in maritime counter-piracy and maritime cybersecurity governance, including why and how they provide effective solutions for enhancing critical maritime infrastructure protection. Finally, we assess the ongoing institution building for CMIP in Europe. While we focus on the European experience, our discussion on designing institutions carries forward lessons for CMIP in other regions, too.

Entity and Relation Linking for Knowledge Graph Question Answering Using Gradual Searching

Knowledge graph question answering (KGQA) systems have an important role in retrieving data from a knowledge graph (KG). With the system, regular users can access data from a KG without the need to construct a formal SPARQL query. KGQA systems receive a natural language question (NLQ) and translate it into a SPARQL query through three main tasks, namely, entity and relation detection, entity and relation linking, and query construction. However, the translation is not trivial due to lexical gaps and entity ambiguity that may occur during entity or relation linking. This research proposed an approach based on multiclass classification of NLQ whose entity occurrences are detected into categories based on KG relations to address the lexical gap challenge. Next, to solve the entity ambiguity challenge, this research proposed a three-stage searching procedure to determine appropriate KG entities associated with the NLQ entities, given the correspondence between the NLQ and a particular KG relation. This three-stage searching consisted of text-based searching, vector-based searching, and entity and relation pairing. The proposed approach was evaluated on the SimpleQuestions and LC-QuAD 2.0 datasets. The experiments demonstrated that the proposed approach outperformed the state-of-the-art baseline. For the relation linking task, the proposed approach reached 89.87% and 74.83% recall for the SimpleQuestions and LC-QuAD 2.0, respectively. This approach also achieved 91.74% and 61.96% recall on the entity linking tasks for the SimpleQuestions and LC-QuAD 2.0, respectively.

Visual Question Answering based Educational Tool for Medical Students using Cross-ViT

Abstract: This paper introduces an advanced approach to medical visual question answering (VQA) using the Cross-ViT architecture. The model employs a dual-branch method to extract multi-scale feature representations from images, utilizing cross-attention mechanisms to enhance visual features. By integrating Stacked Attention Networks (SAN) and leveraging semantic extraction from LSTM for textual data, the model shows significant performance improvements. Experiments on various biomedical VQA tasks demonstrate notable improvements in retrieval accuracy and image-text correlation. The study highlights the potential of medical VQA systems to transform healthcare delivery, improve diagnostic accuracy, and facilitate patient engagement and education, with promising future applications in telemedicine, surgery assistance, and integration with electronic health records

Comparison of Sentences using POS Tagging Tool under Subjective Examination

The Question answering system is used to generate the correct result that is asked by humans in natural language. In an online examination system, most of the work has been done but still, problems occur in preprocessing i.e. Part Of Speech (POS). POS tagger is used to properly tag each word in the sentences. In this paper, we used two datasets i.e. TREC DATA and data collected from the student. We apply the POS tagger to both datasets and compare the result. For generating the POS tagger we used NLTK and spaCy libraries for comparison. We observed that using those libraries the same word has a different tag. Using both tools, we computed the difference between the words and assigned the count to the POS tagging on that result, we calculate the accuracy of both libraries. The result shows that the spaCy library is best for POS tagging because it generates more correct results as compared to NLTK.

GRAAL: Graph-Based Retrieval for Collecting Related Passages across Multiple Documents

Finding passages related to a sentence over a large collection of text documents is a fundamental task for claim verification and open-domain question answering. For instance, a common approach for verifying a claim is to extract short snippets of relevant text from a collection of reference documents and provide them as input to a natural language inference machine that determines whether the claim can be deduced or refuted. Available approaches struggle when several pieces of evidence from different documents need to be combined to make an inference, as individual documents often have a low relevance with the input and are therefore excluded. We propose GRAAL (GRAph-based retrievAL), a novel graph-based approach that outlines the relevant evidence as a subgraph of a large graph that summarizes the whole corpus. We assess the validity of this approach by building a large graph that represents co-occurring entity mentions on a corpus of Wikipedia pages and using this graph to identify candidate text relevant to a claim across multiple pages. Our experiments on a subset of FEVER, a popular benchmark, show that the proposed approach is effective in identifying short passages related to a claim from multiple documents.

Fuzzy ensemble of fined tuned BERT models for domain-specific sentiment analysis of software engineering dataset.

Software engineers post their opinions about various topics on social media that can be collectively mined using Sentiment Analysis. Analyzing this opinion is useful because it can provide insight into developers' feedback about various tools and topics. General-purpose sentiment analysis tools do not work well in the software domain because most of these tools are trained on movies and review datasets. Therefore, efforts are underway to develop domain-specific sentiment analysis tools for the Software Engineering (SE) domain. However, existing domain-specific tools for SE struggle to compute negative and neutral sentiments and can not be used on all SE datasets. This work uses a hybrid technique based on deep learning and a fine-tuned BERT model, i.e., Bert-Base, Bert-Large, Bert-LSTM, Bert-GRU, and Bert-CNN presented that is adapted as a domain-specific sentiment analysis tool for Community Question Answering datasets (named as Fuzzy Ensemble). Five different variants of fine-tuned BERT on the SE dataset are developed, and an ensemble of these fine-tuned models is taken using fuzzy logic. The trained model is evaluated on four publicly available benchmark datasets, i.e., Stack Overflow, JavaLib, Jira, and Code Review, using various evaluation metrics. The fuzzy Ensemble model is also compared with the state-of-the-art sentiment analysis tools for the software engineering domain, i.e., SentiStrength-SE, Senti4SD, SentiCR, and Generative Pre-Training Transformer (GPT). GPT mode is fine-tuned by the authors for domain-specific sentiment analysis. The Fuzzy Ensemble model covers the limitation of existing tools and improve accuracy to predict neutral sentiments even on diverse dataset. The fuzzy Ensemble model performs superior to state-of-the-art tools by achieving a maximum F1-score of 0.883.

Modular dual-stream visual fusion network for visual question answering

Modular dual-stream visual fusion network for visual question answering

GEUKE: A geographic entities uniformly explicit knowledge embedding model

AbstractKnowledge embedding for geographic knowledge graphs can effectively improve computational efficiency and provide support for knowledge reasoning, knowledge answering and other applications of knowledge graphs. To maintain a more comprehensive understanding of spatial features through knowledge embedding, it is crucial to integrate the representation and computation of various entity types, encompassing points, lines, and polygons. This article proposes a geographic entities uniformly explicit knowledge embedding model (GEUKE). In GEUKE, spatial data of point, line, and polygon‐type geographic entities are expressed in the form of subgraphs, and space embedding is generated using a SubGNN‐based uniform spatial feature encoder. GEUKE improves the energy function in TransE to train spatial feature‐based embedding and structural‐based embedding of geographic entities into a unified vector space. Experimental results show that GEUKE has higher performance than TransE, TransH, TransD, and TransE‐GDR on link prediction and triple classification task. Within the spatial feature embedding process, GEUKE effectively preserves the inherent features of entities, encompassing location, neighborhood, and structural attributes, while simultaneously ensuring a coherent spatial data representation across all three entity types: points, lines, and polygons. By maintaining the spatial features of geographic entities and their interrelations, this capability unleashes the full potential of applications such as knowledge reasoning and geospatial question answering in a manner that is conducive to diverse geospatial scenarios.

Research on the Application and Optimization Strategies of Deep Learning in Large Language Models

The development of deep learning technology provides new opportunities for the construction and application of large language models. This paper systematically explores the current application status and optimization strategies of deep learning in large language models. The paper introduces the basic concepts and principles of deep learning and large language models, focusing on language representation methods, model architectures, and application cases. Addressing the challenges faced by large language models, the paper analyzes in detail optimization strategies such as model compression and acceleration, transfer learning and domain adaptation, data augmentation, and unsupervised learning. Through experiments on multiple benchmark datasets, the superior performance of deep learning models in tasks such as language understanding, text classification, named entity recognition, and question answering is confirmed, demonstrating their enormous potential in large language models. At the same time, the paper discusses the limitations of existing methods and proposes future research directions. This paper provides a comprehensive overview and insights into the application of deep learning in large language models, which is of great significance for advancing natural language processing technology.