A Question Routing Technique Using Deep Neural Network for Communities of Question Answering

Azzam, Amr; El-Tazi, Neamat; Hossny, Ahmad Hany

doi:10.1007/978-3-319-55753-3_3

Cited by 9 publications

(8 citation statements)

References 17 publications

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“…This method uses the concept of distributional semantic, where the co-occurring signals are likely to be semantically associated [22]. Example 1: Original Tweet: Protesters may be unmasked in wake of Coburg clash https://t.co/djjVIfzO3e (News) #melbourne #victoria Assuming a time frame of 20 days word-pair: [2,3,3,4,5,3,2,3,8,3,3,1,3,9,3,1,2,4,5,1] Spikes (B OW ): [0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0] Events(GT ): [0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0]…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…This process models the relationship between topics and hashtags in tweets, and uses them both as features for event discovery [41]. Azzam et al used deep learning and cosine similarity to understand short text posts in communities of question answering [3], [4]. Also, Hossny et al used inductive logic programming to understand short sentences from news for translation purposes [16] C. Sentiment analysis approaches…”

Section: B Topic Modelling Approachesmentioning

confidence: 99%

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Event Detection in Twitter: A Keyword Volume Approach

Hossny

Mitchell

2018

2018 IEEE International Conference on Data Mining Workshops (ICDMW)

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Event detection using social media streams needs a set of informative features with strong signals that need minimal preprocessing and are highly associated with events of interest. Identifying these informative features as keywords from Twitter is challenging, as people use informal language to express their thoughts and feelings. This informality includes acronyms, misspelled words, synonyms, transliteration and ambiguous terms. In this paper, we propose an efficient method to select the keywords frequently used in Twitter that are mostly associated with events of interest such as protests. The volume of these keywords is tracked in real time to identify the events of interest in a binary classification scheme. We use keywords within wordpairs to capture the context. The proposed method is to binarize vectors of daily counts for each word-pair by applying a spike detection temporal filter, then use the Jaccard metric to measure the similarity of the binary vector for each word-pair with the binary vector describing event occurrence. The top n wordpairs are used as features to classify any day to be an event or non-event day. The selected features are tested using multiple classifiers such as Naive Bayes, SVM, Logistic Regression, KNN and decision trees. They all produced AUC ROC scores up to 0.91 and F1 scores up to 0.79. The experiment is performed using the English language in multiple cities such as Melbourne, Sydney and Brisbane as well as the Indonesian language in Jakarta. The two experiments, comprising different languages and locations, yielded similar results.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: B Topic Modelling Approachesmentioning

confidence: 99%

Event Detection in Twitter: A Keyword Volume Approach

Hossny

Mitchell

2018

2018 IEEE International Conference on Data Mining Workshops (ICDMW)

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“…For training, the Gibbs-LDA++ [37] with topic size K=100 is applied, and we set the LDA hyper-parameters to α = 0.5 and β = 0.1. (2) QR-DSSM [48] was proposed by Azzam et al by directly applying the Deep Semantic Similarity Model [26]. In our experiment, two fully connected DNNs which contain two hidden layers with 300 nodes in each are applied to learn the feature vectors, and cosine similarity calculations are conducted after the output layer vectors.…”

Section: Content-based Methodsmentioning

confidence: 99%

Expert Recommendation in Community Question Answering via Heterogeneous Content Network Embedding

Li¹,

Li²,

Li³

et al. 2023

Computers, Materials &Amp; Continua

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Expert Recommendation (ER) aims to identify domain experts with high expertise and willingness to provide answers to questions in Community Question Answering (CQA) web services. How to model questions and users in the heterogeneous content network is critical to this task. Most traditional methods focus on modeling questions and users based on the textual content left in the community while ignoring the structural properties of heterogeneous CQA networks and always suffering from textual data sparsity issues. Recent approaches take advantage of structural proximities between nodes and attempt to fuse the textual content of nodes for modeling. However, they often fail to distinguish the nodes' personalized preferences and only consider the textual content of a part of the nodes in network embedding learning, while ignoring the semantic relevance of nodes. In this paper, we propose a novel framework that jointly considers the structural proximity relations and textual semantic relevance to model users and questions more comprehensively. Specifically, we learn topology-based embeddings through a hierarchical attentive network learning strategy, in which the proximity information and the personalized preference of nodes are encoded and preserved. Meanwhile, we utilize the node's textual content and the text correlation between adjacent nodes to build the content-based embedding through a meta-context-aware skip-gram model. In addition, the user's relative answer quality is incorporated to promote the ranking performance. Experimental results show that our proposed framework consistently and significantly outperforms the state-of-the-art baselines on three real-world datasets by taking the deep semantic understanding and structural feature learning together. The performance of the proposed work is analyzed in terms of MRR, P@K, and MAP and is proven to be more advanced than the existing methodologies.

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“…Cheng modeled question routing as a multi-objective ranking problem (Cheng et al, 2017). Azzam proposed using deep neural networks to extract semantically similar features, which are used to rank answerers by relevance (Azzam et al, 2017). Roy used the past questions and answers to find the potential answerers (Roy et al, 2018).…”

Section: Extracting Core Questions In Cqamentioning

confidence: 99%

Extracting core questions in community question answering based on particle swarm optimization

Chen

2019

DTA

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Purpose A large number of questions are posted on community question answering (CQA) websites every day. Providing a set of core questions will ease the question overload problem. These core questions should cover the main content of the original question set. There should be low redundancy within the core questions and a consistent distribution with the original question set. The paper aims to discuss these issues. Design/methodology/approach In the paper, a method named QueExt method for extracting core questions is proposed. First, questions are modeled using a biterm topic model. Then, these questions are clustered based on particle swarm optimization (PSO). With the clustering results, the number of core questions to be extracted from each cluster can be determined. Afterwards, the multi-objective PSO algorithm is proposed to extract the core questions. Both PSO algorithms are integrated with operators in genetic algorithms to avoid the local optimum. Findings Extensive experiments on real data collected from the famous CQA website Zhihu have been conducted and the experimental results demonstrate the superior performance over other benchmark methods. Research limitations/implications The proposed method provides new insight into and enriches research on information overload in CQA. It performs better than other methods in extracting core short text documents, and thus provides a better way to extract core data. The PSO is a novel method used for selecting core questions. The research on the application of the PSO model is expanded. The study also contributes to research on PSO-based clustering. With the integration of K-means++, the key parameter number of clusters is optimized. Originality/value The novel core question extraction method in CQA is proposed, which provides a novel and efficient way to alleviate the question overload. The PSO model is extended and novelty used in selecting core questions. The PSO model is integrated with K-means++ method to optimize the number of clusters, which is just the key parameter in text clustering based on PSO. It provides a new way to cluster texts.

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A Question Routing Technique Using Deep Neural Network for Communities of Question Answering

Cited by 9 publications

References 17 publications

Event Detection in Twitter: A Keyword Volume Approach

Event Detection in Twitter: A Keyword Volume Approach

Expert Recommendation in Community Question Answering via Heterogeneous Content Network Embedding

Extracting core questions in community question answering based on particle swarm optimization

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