Graph and Neural Network-Based Intelligent Conversation System

2019 ◽  
pp. 339-357 ◽  
Author(s):  
Anuja Arora ◽  
Aman Srivastava ◽  
Shivam Bansal
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
System Approach ◽  
Domain Knowledge ◽  
Research Work ◽  
Graph Model ◽  
Machine Intelligence ◽  
Knowledge Graph ◽  
Feature Engineering ◽  
Graph Based Model

The conventional approach to build a chatbot system uses the sequence of complex algorithms and productivity of these systems depends on order and coherence of algorithms. This research work introduces and showcases a deep learning-based conversation system approach. The proposed approach is an intelligent conversation model approach which conceptually uses graph model and neural conversational model. The proposed deep learning-based conversation system uses neural conversational model over knowledge graph model in a hybrid manner. Graph-based model answers questions written in natural language using its intent in the knowledge graph and neural conversational model converses answer based on conversation content and conversation sequence order. NLP is used in graph model and neural conversational model uses natural language understanding and machine intelligence. The neural conversational model uses seq2seq framework as it requires less feature engineering and lacks domain knowledge. The results achieved through the authors' approach are competitive with solely used graph model results.

scholarly journals An Efficient SMOTE-Based Deep Learning Model for Heart Attack Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Muhammad Waqar ◽  
Hassan Dawood ◽  
Hussain Dawood ◽  
Nadeem Majeed ◽  
Ameen Banjar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Heart Attack ◽  
High Reliability ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Feature Engineering ◽  
Unequal Distribution ◽  
The Given

Cardiac disease treatments are often being subjected to the acquisition and analysis of vast quantity of digital cardiac data. These data can be utilized for various beneficial purposes. These data’s utilization becomes more important when we are dealing with critical diseases like a heart attack where patient life is often at stake. Machine learning and deep learning are two famous techniques that are helping in making the raw data useful. Some of the biggest problems that arise from the usage of the aforementioned techniques are massive resource utilization, extensive data preprocessing, need for features engineering, and ensuring reliability in classification results. The proposed research work presents a cost-effective solution to predict heart attack with high accuracy and reliability. It uses a UCI dataset to predict the heart attack via various machine learning algorithms without the involvement of any feature engineering. Moreover, the given dataset has an unequal distribution of positive and negative classes which can reduce performance. The proposed work uses a synthetic minority oversampling technique (SMOTE) to handle given imbalance data. The proposed system discarded the need of feature engineering for the classification of the given dataset. This led to an efficient solution as feature engineering often proves to be a costly process. The results show that among all machine learning algorithms, SMOTE-based artificial neural network when tuned properly outperformed all other models and many existing systems. The high reliability of the proposed system ensures that it can be effectively used in the prediction of the heart attack.

scholarly journals Knowledge Graph Question and Answer System for Mechanical Intelligent Manufacturing Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Miaoyuan Shi
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
Intelligent Manufacturing ◽  
Focus Of Attention ◽  
Knowledge Graph ◽  
Single Entity ◽  
Question And Answer ◽  
Natural Language Query ◽  
The One ◽  
Answering Questions

With the development of deep learning and its wide application in the field of natural language, the question and answer research of knowledge graph based on deep learning has gradually become the focus of attention. After that, the natural language query is converted into a structured query sentence to identify the entities and attributes in the user’s natural language query and the specified entities and attributes are used to retrieve answers to the knowledge graph. Using the advantage of deep learning in capturing sentence information, it incorporates the attention mechanism to obtain the semantic vector of the relevant attributes in the query and uses the parameter sharing mechanism to insert candidate attributes into the triple in the same model to obtain the semantic vector of typical candidates. The experiment measured that under the 100,000 RDF dataset, the single entity query of the MIQE model does not exceed 3 seconds, and the connection query does not exceed 5 seconds. Under the one-million RDF dataset, the single entity query of the MIQE model does not exceed 8 seconds, and the connection query will not be more than 10 seconds. Experimental data show that the system of knowledge-answering questions of engineering of intelligent construction based on deep learning has good horizontal scalability.

BUILD KNOWLEDGE GRAPH FROM HETEROGENEOUS DOCUMENTS

2021 ◽  
Vol 47 (05) ◽  
Author(s):  
NGUYỄN CHÍ HIẾU
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Semantic Analysis ◽  
Knowledge Graph ◽  
Question Answering Systems ◽  
Knowledge Graphs

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents.  We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain

scholarly journals Deep Learning Based Question Answering Search Engine

2021 ◽  
pp. 25-32
Author(s):  
Mrunal Malekar
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language ◽  
Search Engine ◽  
Language Processing ◽  
Question Answering ◽  
Research Work ◽  
Construction Company ◽  
Exact Answer ◽  
Search For Information

Domain based Question Answering is concerned with building systems which provide answers to natural language questions that are asked specific to a domain. It comes under Information Retrieval and Natural language processing. Using Information Retrieval, one can search for the relevant documents which may contain the answer but it won’t give the exact answer for the question asked. In the presented work, a question answering search engine has been developed which first finds out the relevant documents from a huge textual document data of a construction company and then goes a step beyond to extract answer from the extracted document. The robust question answering system developed uses Elastic Search for Information Retrieval [paragraphs extraction] and Deep Learning for answering the question from the short extracted paragraph. It leverages BERT Deep Learning Model to understand the layers and representations between the question and answer. The research work also focuses on how to improve the search accuracy of the Information Retrieval based Elastic Search engine which returns the relevant documents which may contain the answer.

scholarly journals Marrying Medical Domain Knowledge With Deep Learning on Electronic Health Records: A Deep Visual Analytics Approach (Preprint)

2020 ◽  
Author(s):  
Rui Li ◽  
Changchang Yin ◽  
Samuel Yang ◽  
Buyue Qian ◽  
Ping Zhang
Keyword(s):  
Heart Failure ◽  
Deep Learning ◽  
Risk Prediction ◽  
Visual Analytics ◽  
Domain Knowledge ◽  
Knowledge Graph ◽  
Data Set ◽  
Medical Domain ◽  
Clinical Risks

BACKGROUND Deep learning models have attracted significant interest from health care researchers during the last few decades. There have been many studies that apply deep learning to medical applications and achieve promising results. However, there are three limitations to the existing models: (1) most clinicians are unable to interpret the results from the existing models, (2) existing models cannot incorporate complicated medical domain knowledge (eg, a disease causes another disease), and (3) most existing models lack visual exploration and interaction. Both the electronic health record (EHR) data set and the deep model results are complex and abstract, which impedes clinicians from exploring and communicating with the model directly. OBJECTIVE The objective of this study is to develop an interpretable and accurate risk prediction model as well as an interactive clinical prediction system to support EHR data exploration, knowledge graph demonstration, and model interpretation. METHODS A domain-knowledge–guided recurrent neural network (DG-RNN) model is proposed to predict clinical risks. The model takes medical event sequences as input and incorporates medical domain knowledge by attending to a subgraph of the whole medical knowledge graph. A global pooling operation and a fully connected layer are used to output the clinical outcomes. The middle results and the parameters of the fully connected layer are helpful in identifying which medical events cause clinical risks. DG-Viz is also designed to support EHR data exploration, knowledge graph demonstration, and model interpretation. RESULTS We conducted both risk prediction experiments and a case study on a real-world data set. A total of 554 patients with heart failure and 1662 control patients without heart failure were selected from the data set. The experimental results show that the proposed DG-RNN outperforms the state-of-the-art approaches by approximately 1.5%. The case study demonstrates how our medical physician collaborator can effectively explore the data and interpret the prediction results using DG-Viz. CONCLUSIONS In this study, we present DG-Viz, an interactive clinical prediction system, which brings together the power of deep learning (ie, a DG-RNN–based model) and visual analytics to predict clinical risks and visually interpret the EHR prediction results. Experimental results and a case study on heart failure risk prediction tasks demonstrate the effectiveness and usefulness of the DG-Viz system. This study will pave the way for interactive, interpretable, and accurate clinical risk predictions.

scholarly journals Enhancing Profile and Context Aware Relevant Food Search through Knowledge Graphs

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1228 ◽  
Author(s):  
Unai Zulaika ◽  
Asier Gutiérrez ◽  
Diego López-de-Ipiña
Keyword(s):  
Natural Language ◽  
Knowledge Graph ◽  
Human Intelligence ◽  
Context Aware ◽  
Food Items ◽  
Usable Information ◽  
Cognitive Services ◽  
Knowledge Graphs ◽  
Ibm Watson ◽  
Graph Based Model

Foodbar is a Cloud-based gastroevaluation solution, leveraging IBM Watson cognitive services. It brings together machine and human intelligence to enable cognitive gastroevaluation of “tapas” or “pintxos” , i.e., small miniature bites or dishes. Foodbar matchmakes users’ profiles, preferences and context against an elaborated knowledge graph based model of user and machine generated information about food items. This paper reasons about the suitability of this novel way of modelling heterogeneous, with diverse degree of veracity, information to offer more stakeholder satisfying knowledge exploitation solutions, i.e., those offering more relevant and elaborated, directly usable, information to those that want to take decisions regarding food in miniature. An evaluation of the information modelling power of such approach is performed highlighting why such model can offer better more relevant and enriched answers to natural language questions posed by users.

Part-of-Speech (POS) Tagging Using Deep Learning-Based Approaches on the Designed Khasi POS Corpus

2022 ◽  
Vol 21 (3) ◽  
pp. 1-24
Author(s):  
Sunita Warjri ◽  
Partha Pakray ◽  
Saralin A. Lyngdoh ◽  
Arnab Kumar Maji
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Research Work ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Corpus Size ◽  
Increase In Accuracy ◽  
Pos Tagger

Part-of-speech (POS) tagging is one of the research challenging fields in natural language processing (NLP). It requires good knowledge of a particular language with large amounts of data or corpora for feature engineering, which can lead to achieving a good performance of the tagger. Our main contribution in this research work is the designed Khasi POS corpus. Till date, there has been no form of any kind of Khasi corpus developed or formally developed. In the present designed Khasi POS corpus, each word is tagged manually using the designed tagset. Methods of deep learning have been used to experiment with our designed Khasi POS corpus. The POS tagger based on BiLSTM, combinations of BiLSTM with CRF, and character-based embedding with BiLSTM are presented. The main challenges of understanding and handling Natural Language toward Computational linguistics to encounter are anticipated. In the presently designed corpus, we have tried to solve the problems of ambiguities of words concerning their context usage, and also the orthography problems that arise in the designed POS corpus. The designed Khasi corpus size is around 96,100 tokens and consists of 6,616 distinct words. Initially, while running the first few sets of data of around 41,000 tokens in our experiment the taggers are found to yield considerably accurate results. When the Khasi corpus size has been increased to 96,100 tokens, we see an increase in accuracy rate and the analyses are more pertinent. As results, accuracy of 96.81% is achieved for the BiLSTM method, 96.98% for BiLSTM with CRF technique, and 95.86% for character-based with LSTM. Concerning substantial research from the NLP perspectives for Khasi, we also present some of the recently existing POS taggers and other NLP works on the Khasi language for comparative purposes.

scholarly journals Deep learning: from speech recognition to language and multimodal processing

2016 ◽  
Vol 5 ◽  
Author(s):  
Li Deng
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Speech Recognition ◽  
Natural Language ◽  
Domain Knowledge ◽  
Generative Models ◽  
Seamless Integration ◽  
Continuous Space ◽  
Multimodal Processing ◽  
Board Industry

While artificial neural networks have been in existence for over half a century, it was not until year 2010 that they had made a significant impact on speech recognition with a deep form of such networks. This invited paper, based on my keynote talk given at Interspeech conference in Singapore in September 2014, will first reflect on the historical path to this transformative success, after providing brief reviews of earlier studies on (shallow) neural networks and on (deep) generative models relevant to the introduction of deep neural networks (DNN) to speech recognition several years ago. The role of well-timed academic-industrial collaboration is highlighted, so are the advances of big data, big compute, and the seamless integration between the application-domain knowledge of speech and general principles of deep learning. Then, an overview is given on sweeping achievements of deep learning in speech recognition since its initial success. Such achievements, summarized into six major areas in this article, have resulted in across-the-board, industry-wide deployment of deep learning in speech recognition systems. Next, more challenging applications of deep learning, natural language and multimodal processing, are selectively reviewed and analyzed. Examples include machine translation, knowledgebase completion, information retrieval, and automatic image captioning, where fresh ideas from deep learning, continuous-space embedding in particular, are shown to be revolutionizing these application areas albeit with less rapid pace than for speech and image recognition. Finally, a number of key issues in deep learning are discussed, and future directions are analyzed for perceptual tasks such as speech, image, and video, as well as for cognitive tasks involving natural language.

Developing a Vietnamese Tourism Question Answering System Using Knowledge Graph and Deep Learning

2021 ◽  
Vol 20 (5) ◽  
pp. 1-18
Author(s):  
Phuc Do ◽  
Truong H. V. Phan ◽  
Brij B. Gupta
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
Question Answering ◽  
Knowledge Graph ◽  
Test Set ◽  
Tourist Attractions ◽  
Question Answering System ◽  
Tourism Websites

In recent years, Question Answering (QA) systems have increasingly become very popular in many sectors. This study aims to use a knowledge graph and deep learning to develop a QA system for tourism in Vietnam. First, the QA system replies to a user's question about a place in Vietnam. Then, the QA describes it in detail such as when the place was discovered, why the place's name was called like that, and so on. Finally, the system recommends some related tourist attractions to users. Meanwhile, deep learning is used to solve a simple natural language answer, and a knowledge graph is used to infer a natural language answering list related to entities in the question. The study experiments on a manual dataset collected from Vietnamese tourism websites. As a result, the QA system combining the two above approaches provides more information than other systems have done before. Besides that, the system gets 0.83 F1, 0.87 precision on the test set.

scholarly journals Marrying Medical Domain Knowledge With Deep Learning on Electronic Health Records: A Deep Visual Analytics Approach

10.2196/20645 ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. e20645
Author(s):  
Rui Li ◽  
Changchang Yin ◽  
Samuel Yang ◽  
Buyue Qian ◽  
Ping Zhang
Keyword(s):  
Heart Failure ◽  
Deep Learning ◽  
Risk Prediction ◽  
Visual Analytics ◽  
Domain Knowledge ◽  
Knowledge Graph ◽  
Data Set ◽  
Medical Domain ◽  
Clinical Risks

Background Deep learning models have attracted significant interest from health care researchers during the last few decades. There have been many studies that apply deep learning to medical applications and achieve promising results. However, there are three limitations to the existing models: (1) most clinicians are unable to interpret the results from the existing models, (2) existing models cannot incorporate complicated medical domain knowledge (eg, a disease causes another disease), and (3) most existing models lack visual exploration and interaction. Both the electronic health record (EHR) data set and the deep model results are complex and abstract, which impedes clinicians from exploring and communicating with the model directly. Objective The objective of this study is to develop an interpretable and accurate risk prediction model as well as an interactive clinical prediction system to support EHR data exploration, knowledge graph demonstration, and model interpretation. Methods A domain-knowledge–guided recurrent neural network (DG-RNN) model is proposed to predict clinical risks. The model takes medical event sequences as input and incorporates medical domain knowledge by attending to a subgraph of the whole medical knowledge graph. A global pooling operation and a fully connected layer are used to output the clinical outcomes. The middle results and the parameters of the fully connected layer are helpful in identifying which medical events cause clinical risks. DG-Viz is also designed to support EHR data exploration, knowledge graph demonstration, and model interpretation. Results We conducted both risk prediction experiments and a case study on a real-world data set. A total of 554 patients with heart failure and 1662 control patients without heart failure were selected from the data set. The experimental results show that the proposed DG-RNN outperforms the state-of-the-art approaches by approximately 1.5%. The case study demonstrates how our medical physician collaborator can effectively explore the data and interpret the prediction results using DG-Viz. Conclusions In this study, we present DG-Viz, an interactive clinical prediction system, which brings together the power of deep learning (ie, a DG-RNN–based model) and visual analytics to predict clinical risks and visually interpret the EHR prediction results. Experimental results and a case study on heart failure risk prediction tasks demonstrate the effectiveness and usefulness of the DG-Viz system. This study will pave the way for interactive, interpretable, and accurate clinical risk predictions.

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