A Shallow Neural Network Based Short Text Classifier for Medical Community Question Answering System

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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Answer Selection in Community Question Answering Using LSTM

10.4018/978-1-7998-8061-5.ch008 ◽

2021 ◽

pp. 153-165

Author(s):

Saman Qureshi ◽

Sri Khetwat Saritha ◽

D. Kishan

Keyword(s):

Neural Network ◽

Question Answering ◽

Attention Mechanism ◽

Generalization Ability ◽

Mechanism Model ◽

Community Question Answering ◽

Informative Part ◽

External Sources

Websites like Quora, Yahoo! Answers, and Reddit are examples of community question answering (CQA) systems that enable users to ask questions as well as to answer questions. Answer selection is the most challenging task in CQA systems to get the good and relevant answer for the user questions. The shortcomings in the current approaches are lexical gap between text pairs, dependency on external sources, and manual features which lead to lack of generalization ability. These shortcomings are resolved by already proposed work, but they lack generalization, and their performance is not satisfying. Whereas to focus on rich quality answers, attention mechanism can be integrates with neural network. This chapter proposes two models BLSTM and BLSTM with attention mechanism. Attention mechanism aligns question to the answer with the answer's more informative part. So, when it is applied in the model, BLSTM with attention mechanism model surpasses the top approaches.

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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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A Question Answering System on Holy Quran Translation Based on Question Expansion Technique and Neural Network Classification

Journal of Computer Science ◽

10.3844/jcssp.2016.169.177 ◽

2016 ◽

Vol 12 (3) ◽

pp. 169-177 ◽

Cited By ~ 7

Author(s):

Suhaib Kh. Hamed ◽

Mohd Juzaiddin Ab Aziz

Keyword(s):

Neural Network ◽

Question Answering ◽

Question Answering System ◽

Neural Network Classification ◽

Expansion Technique ◽

Holy Quran

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Short Text Similarity Computing Method towards Agriculture Question and Answering Systems

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.1309 ◽

2013 ◽

Vol 756-759 ◽

pp. 1309-1313 ◽

Cited By ~ 1

Author(s):

Bing Jie Sun ◽

Zhi Chao Liang ◽

Qing Tian Zeng ◽

Hua Zhao ◽

Wei Jian Ni ◽

...

Keyword(s):

Question Answering ◽

Extended Version ◽

Text Similarity ◽

Short Text ◽

Question Answering System ◽

The Core ◽

Similarity Calculation ◽

Core Issue ◽

Short Text Similarity ◽

Computing Method

Text similarity computing is the core issue that question-answering system needs to solve. It is mainly used to filter out the existed problems which are similar to the users questions from database. Because of the low recall of domain keywords in domain text similarity computing based on traditional semantic dictionary, this paper proposed a short text similarity computing method in the field of agriculture based on the extended version of <<Tongyicicilin>> which referred to as <<CiLin>>. This paper propose to consider both the similarity and correlation when calculate the words final similarity. The experimental results show that the proposed short text similarity computing method resolve the problem of the low recall of domain words in traditional semantic dictionary well, and improve the similarity calculation performance of high relevant keywords greatly.

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