Research on Design of Automatic Question Answering System Based on Convolutional Neural Network

As a typical cyber-physical-social system (CPSS), the waste collection system profoundly changes the current waste processing mode and greatly relieves the dilemma of waste disposal. However, the existing waste collection system does not provide the function that guides people to deliver the waste into the correct trash bin. In order to improve the efficiency of waste collection system, we propose an automatic question answering system based on convolutional neural network (CNN) to help people classify waste correctly. The construction process of automatic question answering system is divided into the following steps. We first construct a question answering dataset about waste classification, in which question answering pairs from the four waste categories (recyclable waste, harmful waste, dry waste, and wet waste) are included. After the dataset is constructed, we perform text preprocessing on the dataset, which includes denoising, Chinese word segmentation, and removing stop words. After text preprocessing, we use the Word2vec model as feature representation. Then, we construct a CNN and utilize the word embeddings as an input to train model. Finally, we deploy the trained model to the waste collection system, which can answer the question of waste classification that people ask. We also present a comparative analysis of the proposed method and traditional machine learning methods. The experiment shows that the proposed method has higher accuracy of waste classification than that of traditional machine learning methods.

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Pencarian Question-Answer Menggunakan Convolutional Neural Network Pada Topik Agama Berbahasa Indonesia

Jurnal ULTIMATICS ◽

10.31937/ti.v10i1.842 ◽

2018 ◽

Vol 10 (1) ◽

pp. 57-64 ◽

Cited By ~ 1

Author(s):

Rizqa Raaiqa Bintana ◽

Chastine Fatichah ◽

Diana Purwitasari

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Question Answering ◽

Mean Average Precision ◽

Average Precision ◽

Community Based ◽

Network Methods ◽

The Mean ◽

Index Terms ◽

Search Information

Community-based question answering (CQA) is formed to help people who search information that they need through a community. One condition that may occurs in CQA is when people cannot obtain the information that they need, thus they will post a new question. This condition can cause CQA archive increased because of duplicated questions. Therefore, it becomes important problems to find semantically similar questions from CQA archive towards a new question. In this study, we use convolutional neural network methods for semantic modeling of sentence to obtain words that they represent the content of documents and new question. The result for the process of finding the same question semantically to a new question (query) from the question-answer documents archive using the convolutional neural network method, obtained the mean average precision value is 0,422. Whereas by using vector space model, as a comparison, obtained mean average precision value is 0,282. Index Terms—community-based question answering, convolutional neural network, question retrieval

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Static Correlative Filter Based Convolutional Neural Network for Visual Question Answering

2018 IEEE International Conference on Big Data and Smart Computing (BigComp) ◽

10.1109/bigcomp.2018.00087 ◽

2018 ◽

Cited By ~ 1

Author(s):

Lijun Chen ◽

Qinyu Li ◽

Hanli Wang ◽

Yu Long

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Question Answering ◽

Visual Question Answering

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Bayesian Probability and Tanimoto Based Recurrent Neural Network for Question Answering System

Journal of Web Engineering ◽

10.13052/jwe1540-9589.20315 ◽

2021 ◽

Author(s):

Veeraraghavan Jagannathan

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Question Answering ◽

Bayesian Probability ◽

Significant Information ◽

Continuous Increase ◽

Question Answering System ◽

Relevant Result ◽

Proper Nouns ◽

Different Types

Question Answering (QA) has become one of the most significant information retrieval applications. Despite that, most of the question answering system focused to increase the user experience in finding the relevant result. Due to the continuous increase of web content, retrieving the relevant result faces a challenging issue in the Question Answering System (QAS). Thus, an effective Question Classification (QC), and retrieval approach named Bayesian probability and Tanimoto-based Recurrent Neural Network (RNN) are proposed in this research to differentiate the types of questions more efficiently. This research presented an analysis of different types of questions with respect to the grammatical structures. Various patterns are identified from the questions and the RNN classifier is used to classify the questions. The results obtained by the proposed Bayesian probability and Tanimoto-based RNN showed that the syntactic categories related to the domain-specific types of proper nouns, numeral numbers, and the common nouns enable the RNN classifier to reveal better result for different types of questions. However, the proposed approach obtained better performance in terms of precision, recall, and F-measure with the values of 90.14, 86.301, and 90.936 using dataset-2.

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A Hybrid Neural Network BERT-Cap Based on Pre-Trained Language Model and Capsule Network for User Intent Classification

Complexity ◽

10.1155/2020/8858852 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Hai Liu ◽

Yuanxia Liu ◽

Leung-Pun Wong ◽

Lap-Kei Lee ◽

Tianyong Hao

Keyword(s):

Neural Network ◽

Neural Network Model ◽

Question Answering ◽

Semantic Information ◽

Language Model ◽

Dialogue System ◽

Hybrid Neural Network ◽

Question Answering System ◽

User Intent ◽

Vital Component

User intent classification is a vital component of a question-answering system or a task-based dialogue system. In order to understand the goals of users’ questions or discourses, the system categorizes user text into a set of pre-defined user intent categories. User questions or discourses are usually short in length and lack sufficient context; thus, it is difficult to extract deep semantic information from these types of text and the accuracy of user intent classification may be affected. To better identify user intents, this paper proposes a BERT-Cap hybrid neural network model with focal loss for user intent classification to capture user intents in dialogue. The model uses multiple transformer encoder blocks to encode user utterances and initializes encoder parameters with a pre-trained BERT. Then, it extracts essential features using a capsule network with dynamic routing after utterances encoding. Experiment results on four publicly available datasets show that our model BERT-Cap achieves a F1 score of 0.967 and an accuracy of 0.967, outperforming a number of baseline methods, indicating its effectiveness in user intent classification.

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