Text-based question routing for question answering communities via deep learning

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

doi:10.1145/3019612.3019762

Cited by 11 publications

(4 citation statements)

References 15 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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“…In recent years, deep learning neural networks is proposed to address the deep semantic patterns extraction problem, leading to a significant performance improvement. For example, Azzam et al [25] directly applied a deep neural network DSSM (Deep Semantic Similarity Model) [26] to map the question and user's profile to a low-dimensional semantic space for more meaningful representation. It can be said that methods based on neural networks (i.e., CNNs, RNNs, etc.)…”

Section: Related Work 21 Expert Recommendationmentioning

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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“…With the increasing demand of knowledge sharing, the number of questions is continuously increasing and the number of unanswered questions is increasing at a faster rate. For example, Stack Overflow statistics show that the number of questions has a linear increase while the number of unanswered questions has an exponential increase [1]. As a result, it is necessary to solve the problem of question recommendation to guide users to look through the questions they like, make the questions can be answered effectively, and promote the development of the community in a sustainable and harmonious manner.…”

Section: Introductionmentioning

confidence: 99%

AskMe: Joint Individual-level and Community-level Behavior Interaction for Question Recommendation

Guo

Liu

et al. 2021

Preprint

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Questions in Community Question Answering (CQA) sites are recommended to users, mainly based on users' interest extracted from questions that users have answered or have asked. However, there is a general phenomenon that users answer fewer questions while pay more attention to follow questions and vote answers. This can impact the performance when recommending questions to users (for obtaining their answers) by using their historical answering behaviors on existing studies. To address the data sparsity issue, we propose AskMe, which aims to leverage the rich, hybrid behavior interactions in CQA to improve the question recommendation performance. On the one hand, we model the rich correlations between the user's diverse behaviors (e.g., answer, follow, vote) to obtain the individual-level behavior interaction. On the other hand, we model the sophisticated behavioral associations between similar users to obtain the community-level behavior interaction.

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Text-based question routing for question answering communities via deep learning

Cited by 11 publications

References 15 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

AskMe: Joint Individual-level and Community-level Behavior Interaction for Question Recommendation

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