Question Tagging via Graph-guided Ranking

With the increasing prevalence of portable devices and the popularity of community Question Answering (cQA) sites, users can seamlessly post and answer many questions. To effectively organize the information for precise recommendation and easy searching, these platforms require users to select topics for their raised questions. However, due to the limited experience, certain users fail to select appropriate topics for their questions. Thereby, automatic question tagging becomes an urgent and vital problem for the cQA sites, yet it is non-trivial due to the following challenges. On the one hand, vast and meaningful topics are available yet not utilized in the cQA sites; how to model and tag them to relevant questions is a highly challenging problem. On the other hand, related topics in the cQA sites may be organized into a directed acyclic graph. In light of this, how to exploit relations among topics to enhance their representations is critical. To settle these challenges, we devise a graph-guided topic ranking model to tag questions in the cQA sites appropriately. In particular, we first design a topic information fusion module to learn the topic representation by jointly considering the name and description of the topic. Afterwards, regarding the special structure of topics, we propose an information propagation module to enhance the topic representation. As the comprehension of questions plays a vital role in question tagging, we design a multi-level context-modeling-based question encoder to obtain the enhanced question representation. Moreover, we introduce an interaction module to extract topic-aware question information and capture the interactive information between questions and topics. Finally, we utilize the interactive information to estimate the ranking scores for topics. Extensive experiments on three Chinese cQA datasets have demonstrated that our proposed model outperforms several state-of-the-art competitors.

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Supervised attention for answer selection in community question answering

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v9.i2.pp203-211 ◽

2020 ◽

Vol 9 (2) ◽

pp. 203

Author(s):

Thanh Thi Ha ◽

Atsuhiro Takasu ◽

Thanh Chinh Nguyen ◽

Kiem Hieu Nguyen ◽

Van Nha Nguyen ◽

...

Keyword(s):

Language Processing ◽

Question Answering ◽

Irrelevant Information ◽

Social Question ◽

Community Question Answering ◽

Basic Model ◽

Proposed Model ◽

Questions And Answers ◽

Word Attention ◽

Better Than

Answer selection is an important task in Community Question Answering (CQA). In recent years, attention-based neural networks have been extensively studied in various natural language processing problems, including question answering. This paper explores matchLSTM for answer selection in CQA. A lexical gap in CQA is more challenging as questions and answers typical contain multiple sentences, irrelevant information, and noisy expressions. In our investigation, word-by-word attention in the original model does not work well on social question-answer pairs. We propose integrating supervised attention into matchLSTM. Specifically, we leverage lexical-semantic from external to guide the learning of attention weights for question-answer pairs. The proposed model learns more meaningful attention that allows performing better than the basic model. Our performance is among the top on SemEval datasets.

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Neural Abstractive Summarization with Structural Attention

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/514 ◽

2020 ◽

Author(s):

Tanya Chowdhury ◽

Sachin Kumar ◽

Tanmoy Chakraborty

Keyword(s):

Question Answering ◽

Popular Opinion ◽

Document Summarization ◽

Community Question Answering ◽

Proposed Model ◽

Abstractive Summarization

Attentional, RNN-based encoder-decoder architectures have obtained impressive performance on abstractive summarization of news articles. However, these methods fail to account for long term dependencies within the sentences of a document. This problem is exacerbated in multi-document summarization tasks such as summarizing the popular opinion in threads present in community question answering (CQA) websites such as Yahoo! Answers and Quora. These threads contain answers which often overlap or contradict each other. In this work, we present a hierarchical encoder based on structural attention to model such inter-sentence and inter-document dependencies. We set the popular pointer-generator architecture and some of the architectures derived from it as our baselines and show that they fail to generate good summaries in a multi-document setting. We further illustrate that our proposed model achieves significant improvement over the baseline in both single and multi-document summarization settings -- in the former setting, it beats the baseline by 1.31 and 7.8 ROUGE-1 points on CNN and CQA datasets, respectively; in the latter setting, the performance is further improved by 1.6 ROUGE-1 points on the CQA dataset.

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Dual-View Variational Autoencoders for Semi-Supervised Text Matching

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/737 ◽

2019 ◽

Author(s):

Zhongbin Xie ◽

Shuai Ma

Keyword(s):

Question Answering ◽

Fundamental Problem ◽

Sentence Pair ◽

Community Question Answering ◽

Word Level ◽

Sentence Level ◽

Proposed Model ◽

Variational Autoencoder ◽

Matching Models ◽

Text Matching

Semantically matching two text sequences (usually two sentences) is a fundamental problem in NLP. Most previous methods either encode each of the two sentences into a vector representation (sentence-level embedding) or leverage word-level interaction features between the two sentences. In this study, we propose to take the sentence-level embedding features and the word-level interaction features as two distinct views of a sentence pair, and unify them with a framework of Variational Autoencoders such that the sentence pair is matched in a semi-supervised manner. The proposed model is referred to as Dual-View Variational AutoEncoder (DV-VAE), where the optimization of the variational lower bound can be interpreted as an implicit Co-Training mechanism for two matching models over distinct views. Experiments on SNLI, Quora and a Community Question Answering dataset demonstrate the superiority of our DV-VAE over several strong semi-supervised and supervised text matching models.

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Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i05.6266 ◽

2020 ◽

Vol 34 (05) ◽

pp. 7651-7658 ◽

Cited By ~ 2

Author(s):

Yang Deng ◽

Wai Lam ◽

Yuexiang Xie ◽

Daoyuan Chen ◽

Yaliang Li ◽

...

Keyword(s):

Large Scale ◽

Question Answering ◽

State Of The Art ◽

Reading Difficulties ◽

Text Summarization ◽

Essential Information ◽

Joint Learning ◽

Community Question Answering ◽

Proposed Model ◽

Correlation Information

Community question answering (CQA) gains increasing popularity in both academy and industry recently. However, the redundancy and lengthiness issues of crowdsourced answers limit the performance of answer selection and lead to reading difficulties and misunderstandings for community users. To solve these problems, we tackle the tasks of answer selection and answer summary generation in CQA with a novel joint learning model. Specifically, we design a question-driven pointer-generator network, which exploits the correlation information between question-answer pairs to aid in attending the essential information when generating answer summaries. Meanwhile, we leverage the answer summaries to alleviate noise in original lengthy answers when ranking the relevancy degrees of question-answer pairs. In addition, we construct a new large-scale CQA corpus, WikiHowQA, which contains long answers for answer selection as well as reference summaries for answer summarization. The experimental results show that the joint learning method can effectively address the answer redundancy issue in CQA and achieves state-of-the-art results on both answer selection and text summarization tasks. Furthermore, the proposed model is shown to be of great transferring ability and applicability for resource-poor CQA tasks, which lack of reference answer summaries.

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User Embedding for Expert Finding in Community Question Answering

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3441302 ◽

2021 ◽

Vol 15 (4) ◽

pp. 1-16

Author(s):

Negin Ghasemi ◽

Ramin Fatourechi ◽

Saeedeh Momtazi

Keyword(s):

Question Answering ◽

Community Relations ◽

Expert Finding ◽

Community Question Answering ◽

Proposed Model

The number of users who have the appropriate knowledge to answer asked questions in community question answering is lower than those who ask questions. Therefore, finding expert users who can answer the questions is very crucial and useful. In this article, we propose a framework to find experts for given questions and assign them the related questions. The proposed model benefits from users’ relations in a community along with the lexical and semantic similarities between new question and existing answers. Node embedding is applied to the community graph to find similar users. Our experiments on four different Stack Exchange datasets show that adding community relations improves the performance of expert finding models.

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The Implementation of Question Answer System Using Deep Learning

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i1s.1604 ◽

2021 ◽

Vol 12 (1S) ◽

pp. 176-182

Author(s):

Vaishali Fulmal, Et. al.

Keyword(s):

Deep Learning ◽

Language Processing ◽

Clustering Algorithm ◽

Question Answering ◽

Similarity Score ◽

Community Question Answering ◽

The Past ◽

System A ◽

Hierarchical Clustering Algorithm ◽

The One

Question-answer systems are referred to as advanced systems that can be used to provide answers to the questions which are asked by the user. The typical problem in natural language processing is automatic question-answering. The question-answering is aiming at designing systems that can automatically answer a question, in the same way as a human can find answers to questions. Community question answering (CQA) services are becoming popular over the past few years. It allows the members of the community to post as well as answer the questions. It helps users to get information from a comprehensive set of questions that are well answered. In the proposed system, a deep learning-based model is used for the automatic answering of the user’s questions. First, the questions from the dataset are embedded. The deep neural network is trained to find the similarity between questions. The best answer for each question is found as the one with the highest similarity score. The purpose of the proposed system is to design a model that helps to get the answer of a question automatically. The proposed system uses a hierarchical clustering algorithm for clustering the questions.

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HAF-SVG: Hierarchical Stochastic Video Generation with Aligned Features

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/138 ◽

2020 ◽

Author(s):

Zhihui Lin ◽

Chun Yuan ◽

Maomao Li

Keyword(s):

Prediction Accuracy ◽

Spatial Information ◽

State Of The Art ◽

Independence Assumption ◽

Main Challenge ◽

Proposed Model ◽

Generation Network ◽

Multi Level ◽

The One

Stochastic video generation methods predict diverse videos based on observed frames, where the main challenge lies in modeling the complex future uncertainty and generating realistic frames. Numerous of Recurrent-VAE-based methods have achieved state-of-the-art results. However, on the one hand, the independence assumption of the variables of approximate posterior limits the inference performance. On the other hand, although these methods adopt skip connections between encoder and decoder to utilize multi-level features, they still produce blurry generation due to the spatial misalignment between encoder and decoder features at different time steps. In this paper, we propose a hierarchical recurrent VAE with a feature aligner, which can not only relax the independence assumption in typical VAE but also use a feature aligner to enable the decoder to obtain the aligned spatial information from the last observed frames. The proposed model is named Hierarchical Stochastic Video Generation network with Aligned Features, referred to as HAF-SVG. Experiments on Moving-MNIST, BAIR, and KTH datasets demonstrate that hierarchical structure is helpful for modeling more accurate future uncertainty, and the feature aligner is beneficial to generate realistic frames. Besides, the HAF-SVG exceeds SVG on both prediction accuracy and the quality of generated frames.

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A Model of Convolutional Neural Network Combined with External Knowledge to Measure the Question Similarity for Community Question Answering Systems

International Journal of Machine Learning and Computing ◽

10.18178/ijmlc.2021.11.3.1035 ◽

2021 ◽

Vol 11 (3) ◽

pp. 194-201

Author(s):

Van-Tu Nguyen ◽

◽

Anh-Cuong Le ◽

Ha-Nam Nguyen

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Question Answering ◽

Learning Models ◽

Learning Methods ◽

Data Set ◽

External Knowledge ◽

Community Question Answering ◽

Proposed Model

Automatically determining similar questions and ranking the obtained questions according to their similarities to each input question is a very important task to any community Question Answering system (cQA). Various methods have applied for this task including conventional machine learning methods with feature extraction and some recent studies using deep learning methods. This paper addresses the problem of how to combine advantages of different methods into one unified model. Moreover, deep learning models are usually only effective for large data, while training data sets in cQA problems are often small, so the idea of integrating external knowledge into deep learning models for this cQA problem becomes more important. To this objective, we propose a neural network-based model which combines a Convolutional Neural Network (CNN) with features from other methods so that the deep learning model is enhanced with addtional knowledge sources. In our proposed model, the CNN component will learn the representation of two given questions, then combined additional features through a Multilayer Perceptron (MLP) to measure similarity between the two questions. We tested our proposed model on the SemEval 2016 task-3 data set and obtain better results in comparison with previous studies on the same task.

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A Relativistic Newtonian Mechanics Predicts with Precision the Results of Recent Neutrino-Velocity Experiments

JOURNAL OF ADVANCES IN PHYSICS ◽

10.24297/jap.v6i1.1824 ◽

2014 ◽

Vol 6 (1) ◽

pp. 1032-1035 ◽

Cited By ~ 1

Author(s):

Ramzi Suleiman

Keyword(s):

Null Hypothesis ◽

Experimental Studies ◽

Theoretical Models ◽

Newtonian Mechanics ◽

Speed Of Light ◽

Symmetry Principle ◽

Proposed Model ◽

Significant Difference ◽

The One ◽

Complete Collapse

The research on quasi-luminal neutrinos has sparked several experimental studies for testing the "speed of light limit" hypothesis. Until today, the overall evidence favors the "null" hypothesis, stating that there is no significant difference between the observed velocities of light and neutrinos. Despite numerous theoretical models proposed to explain the neutrinos behavior, no attempt has been undertaken to predict the experimentally produced results. This paper presents a simple novel extension of Newton's mechanics to the domain of relativistic velocities. For a typical neutrino-velocity experiment, the proposed model is utilized to derive a general expression for . Comparison of the model's prediction with results of six neutrino-velocity experiments, conducted by five collaborations, reveals that the model predicts all the reported results with striking accuracy. Because in the proposed model, the direction of the neutrino flight matters, the model's impressive success in accounting for all the tested data, indicates a complete collapse of the Lorentz symmetry principle in situation involving quasi-luminal particles, moving in two opposite directions. This conclusion is support by previous findings, showing that an identical Sagnac effect to the one documented for radial motion, occurs also in linear motion.

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Drug Target Group Prediction with Multiple Drug Networks

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207322666190702103927 ◽

2020 ◽

Vol 23 (4) ◽

pp. 274-284 ◽

Cited By ~ 12

Author(s):

Jingang Che ◽

Lei Chen ◽

Zi-Han Guo ◽

Shuaiqun Wang ◽

Aorigele

Keyword(s):

Drug Target ◽

Low Cost ◽

Machine Learning Algorithms ◽

Classification Model ◽

Support Vector ◽

Multiple Drug ◽

Property A ◽

Multiple Networks ◽

Proposed Model ◽

The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

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