Community Question Research Articles

‘Come here, it sang, listen’: Juliana Spahr’s commons poetics in That Winter the Wolf Came

ABSTRACT The work of contemporary American poet, critic, and essayist Juliana Spahr is concerned with politically charged questions of connection, collectivity, and community across disparate ontological domains and ecological boundaries. This essay argues that Spahr’s 2015 poetry collection That Winter the Wolf Came, which has to date garnered less critical attention than her previous work, combines lyric, intertextual reference, asequential narrative, parataxis, and other formal techniques and compositional practices into a commons poetics. This poetics, in turn, allows Spahr to effectively represent a commons which finds space for plural subjects as diverse as the activists of the 2011–2012 Occupy Movement, oil rigs, and Brent geese. Despite Spahr’s own fundamental reservations about the political efficacy of poetry, her unique use of lyric, in particular, cultivates radical connections and oppositional tactics for these plural subjects, offering a vision of a utopian commons beyond capitalism.

KNOWLEDGE-ENHANCED ATTENTIVE LEARNING FOR ANSWER SELECTION IN COMMUNITY QUESTION ANSWERING SYSTEMS

In the community question answering (CQA) system, the answer selection task aims to identify the best answer for a specific question, and thus is playing a key role in enhancing the service quality through recommending appropriate answers for new questions. Recent advances in CQA answer selection focus on enhancing the performance by incorporating the community information, particularly the expertise (previous answers) and authority (position in the social network) of an answerer. However, existing approaches for incorporating such information are limited in (a) only considering either the expertise or the authority, but not both; (b) ignoring the domain knowledge to differentiate topics of previous answers; and (c) simply using the authority information to adjust the similarity score, instead of fully utilizing it in the process of measuring the similarity between segments of the question and the answer. We propose the Knowledge-enhanced Attentive Answer Selection (KAAS) model, which enhances the performance through (a) considering both the expertise and the authority of the answerer; (b) utilizing the human-labeled tags, the taxonomy of the tags, and the votes as the domain knowledge to infer the expertise of the answer; (c) using matrix decomposition of the social network (formed by following-relationship) to infer the authority of the answerer and incorporating such information in the process of evaluating the similarity between segments. Besides, for vertical community, we incorporate an external knowledge graph to capture more professional information for vertical CQA systems. Then we adopt the attention mechanism to integrate the analysis of the text of questions and answers and the aforementioned community information. Experiments with both vertical and general CQA sites demonstrate the superior performance of the proposed KAAS model.

HCA: Hierarchical Compare Aggregate model for question retrieval in community question answering

We address the problem of finding similar historical questions that are semantically equivalent or relevant to an input query question in community question-answering (CQA) sites. One of the main challenges for this task is that questions are usually too long and often contain peripheral information in addition to the main goals of the question. To address this problem, we propose an end-to-end Hierarchical Compare Aggregate (HCA) model that can handle this problem without using any task-specific features. We first split questions into sentences and compare every sentence pair of the two questions using a proposed Word-Level-Compare-Aggregate model called WLCA-model and then the comparison results are aggregated with a proposed Sentence-Level-Compare-Aggregate model to make the final decision. To handle the insufficient training data problem, we propose a sequential transfer learning approach to pre-train the WLCA-model on a large paraphrase detection dataset. Our experiments on two editions of the Semeval benchmark datasets and the domain-specific AskUbuntu dataset show that our model outperforms the state-of-the-art models.

The camp and the question of community

Recent literature in camp geographies has sought to emphasize the significance of political agency among camp residents, particularly in refugee camps, as part of a critical reaction against the highly influential Agambenian conceptual vocabulary of exception and bare life. The concept of community has been integral to this body of work, with diverse accounts of the camp implicitly or explicitly positioning community as the natural scale through which camp resident and inmate agency is formulated, and yet there has hitherto been little research reflecting directly on the meaning that community takes on in the specific context of the camp. In this article we adopt Roberto Esposito's critique of the concept of community to problematize the assumption that camp communities necessarily constitute a space of empowerment and agency for camp residents and inmates. Drawing on Esposito's genealogical account of communitas (2010), whereby community is encountered not in terms of a property shared among individuals but instead as the loss of individuality and other forms of ‘the proper’, we suggest that the implementation of community, while generative of agency, is also fundamental to camp authorities and related regimes of power. Furthermore, we argue that the operation of camp communities includes its own forms of politics that are specific to the exceptional space of the camp and that potentially expose individuals to violence. We develop this argument through an experimental reading of communitas in relation to the two empirical contexts that have been most influential on the trajectory of camp studies within geographical debates, the concentration camp and the refugee camp, represented in this paper by Auschwitz and the contemporary archipelago of Serbian refugee camps respectively. The ambivalent account of power relationships emerging from these readings suggests that Esposito's rendering of community may have important analytic value in investigating the complexity of camp spatialities and the distinctive co-articulation of power and agency therein.

Explanatory Q&A recommendation algorithm in community question answering

PurposeIn community question answering (CQA), people who answer questions assume readers have mastered the content in the answers. Nevertheless, some readers cannot understand all content. Thus, there is a need for further explanation of the concepts that appear in the answers. Moreover, the large number of question and answer (Q&A) documents make manual retrieval difficult. This paper aims to alleviate these issues for CQA websites.Design/methodology/approachIn the paper, an algorithm for recommending explanatory Q&A documents is proposed. Q&A documents are modeled with the biterm topic model (BTM) (Yan et al., 2013). Then, the growing neural gas (GNG) algorithm (Fritzke, 1995) is used to cluster Q&A documents. To train multiple classifiers, three features are extracted from the Q&A categories. Thereafter, an ensemble classification model is constructed to identify the explanatory relationships. Finally, the explanatory Q&A documents are recommended.FindingsThe GNG algorithm shows good clustering performance. The ensemble classification model performs better than other classifiers. The both effect and quality scores of explanatory Q&A recommendations are high. These scores indicate the practicality and good performance of the proposed recommendation algorithm.Research limitations/implicationsThe proposed algorithm alleviates information overload in CQA from the new perspective of recommending explanatory knowledge. It provides new insight into research on recommendations in CQA. Moreover, in practice, CQA websites can use it to help retrieve Q&A documents and facilitate understanding of their contents. However, the algorithm is for the general recommendation of Q&A documents which does not consider individual personalized characteristics. In future work, personalized recommendations will be evaluated.Originality/valueA novel explanatory Q&A recommendation algorithm is proposed for CQA to alleviate the burden of manual retrieval and Q&A overload. The novel GNG clustering algorithm and ensemble classification model provide a more accurate way to identify explanatory Q&A documents. The method of ranking the explanatory Q&A documents improves the effectiveness and quality of the recommendation. The proposed algorithm improves the accuracy and efficiency of retrieving explanatory Q&A documents. It assists users in grasping answers easily.

Domain problem‐solving expert identification in community question answering

AbstractQuestion‐Answering (Q&A) services provide internet users with platforms to exchange knowledge and ideas. The development of Q&A sites, or Community Question Answering (CQA), mainly depends on the high‐quality content continuously contributed by users with high‐level expertise, who can be recognized as experts. Expert finding is an important task for the authorities of Q&A communities to encourage commitment. In a highly competitive market environment, CQA managers have to take measures to retain and nurture users, especially superior contributors. However, current expertise scoring techniques adopted in CQA often give much credit to very active users and fail to identify real experts. This study aims to develop a robust and practical expert identification framework for Q&A communities, by combining well‐designed expertise scoring technique and probabilistic clustering model. With regard to expert identification, a numerical metric of users' expertise is developed as the optimal expert finding strategy, and a clustering algorithm based on Gaussian‐Gamma mixture model (GGMM) is proposed to efficiently distinguish experts from nonexperts. In the experiments, the proposed method is applied to real‐world datasets collected from subcommunities of Stack Exchange Q&A networks. Results obtained from comparative experiments show that our method achieves better performance than the state‐of‐the‐art methods and demonstrate the effectiveness of the proposed framework. The analysis shows that the framework which combines the proposed expertise scoring technique and Gaussian–Gamma mixture clustering model is capable of detecting excellent domain problem‐solving experts who exhibit both domain interest and expertise.

Formative research to design a culturally-appropriate cancer clinical trial education program to increase participation of African American and Latino communities

BackgroundAddressing knowledge deficiencies about cancer clinical trials and biospecimen donation can potentially improve participation among racial and ethnic minorities. This paper describes the formative research process used to design a culturally-appropriate cancer clinical trials education program for African American and Latino communities. We characterized community member feedback and its integration into the program.MethodsWe incorporated three engagement approaches into the formative research process to iteratively develop the program: including community-based organization (CBO) leaders as research team members, conducting focus groups and cognitive interviews with community members as reviewers/consultants, and interacting with two community advisory groups. An iterative-deductive approach was used to analyze focus group data. Qualitative data from advisory groups and community members were compiled and used to finalize the program.ResultsFocus group themes were: 1) Community Perspectives on Overall Presentation; 2) Community Opinions and Questions on the Content of the Presentation; 3) Culturally Specific Issues to Participation in Cancer Clinical Trials; 4) Barriers to Clinical Trial Participation; and 5) Perspectives of Community Health Educators. Feedback was documented during reviews by scientific experts and community members with suggestions to ensure cultural appropriateness using peripheral, evidential, linguistic, sociocultural strategies, and constituent-involving. The final program consisted of two versions (English and Spanish) of a culturally-appropriate slide presentation with speaker notes and videos representing community member and researcher testimonials.ConclusionsIncorporating multiple community engagement approaches into formative research processes can facilitate the inclusion of multiple community perspectives and enhance the cultural-appropriateness of the programs designed to promote cancer clinical trial participation among African Americans and Latinos.

Supervised attention for answer selection in community question answering

<span class="fontstyle0">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 </span><span class="fontstyle2">matchLSTM </span><span class="fontstyle0">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 </span><span class="fontstyle2">matchLSTM</span><span class="fontstyle0">. 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.</span> <br /><br />

Supervised attention for answer selection in community question answering

Supervised attention for answer selection in community question answering

Answer Keyword Generation for Community Question Answering by Multiaspect Gamma–Poisson Matrix Completion

Community question answering (CQA) recommends appropriate answers to existing and new questions. Such answer recommendation is challenging since CQA data are often sparse and decentralized, and lacks sufficient information to generate suitable answers to existing questions. Matching answers to new questions is more challenging in modeling Q/A sparsity, generating answers to cold-start/novel questions, and integrating metadata about Q/A into models, etc. This article addresses these issues by a novel statistical model to automatically generate answer keywords in CQA with multiaspect Gamma-Poisson matrix completion (MAGIC). MAGIC is the first trial in CQA to model multiple aspects of Q/A sentence information in CQA by involving Q/A metadata, Q/A sparsity, and both lexical and semantic Q/A information in a hierarchical Gamma-Poisson model. MAGIC can efficiently generate answer keywords for both existing and new questions against nonnegative matrix factorization (MF), probability MF, and relevant Poisson factorization models w.r.t. recommending appropriate and informative answer keywords.

An integrated retrieval framework for similar questions: Word-semantic embedded label clustering – LDA with question life cycle

Question retrieval is an extremely important research field in Community Question Answering (CQA). Most existing question retrieval methods depend on semantic analysis of questions, whose effectiveness suffers from the short texts of the noise words in the question corpus. In order to recommend the questions with more advanced knowledge to users, the influence of the questions’ popularity should be considered during retrieving questions. To make retrieved questions with both similar semantics and high popularity, we propose an Integrated Retrieval Framework for Similar Questions named Word-semantic Embedded Label Clustering – LDA with Question Life Cycle (WELQLC-QR), consisting of Word-semantic Embedded Label Clustering – LDA (WEL) and Question Life Cycle Optimization Similar Question List Strategy (QLC). Firstly, WEL is proposed for question retrieval from the perspective of semantic matching. It not only overcomes the problem of over-generalization of the semantic information extracted by topic models when facing short questions with multi-levels labels, but also avoids the influence of noise vocabularies during semantic extracting of the questions. Then, based on the internal factors (i.e., the number of comments and answers to the question) and external factors (i.e., programming language ranking information) of questions, QLC constructs a popularity-predicted model to optimize the similar question set searched by WEL, making the final retrieval results both semantically similar and popular. Finally, experiments are conducted on CQADupStack dataset, and results show that the MRR@N of WELQLC-QR model has an average increase of 8.99%, 8.3%, 4.74% and 3.56% compared with that of L-LDA, LC-LDA, BM25 and Word2vec, respectively.

To model of participation of interested parties in governance of the Internet on the international level

This article analyzes the model of participation of many interested parties in governance of the Internet, as well as its definition on the international level and within the doctrine. The goal consists in determining the importance and the role of participation of multiple interested parties in the context of Internet governance, and its correlation to the transnational approach in international law. The model of participation of multiple interested parties is predominantly examined as participation in Internet governance of the actors of international law and private entities. The subject of this research is the provisions of summary documents, summits on the highest level regarding the questions of information community, resolution of the UN General Assembly and other branches of the UN system, provisions of acts of other transnational organizations that are dedicated to development of state policy in the area of Internet governance, as well as doctrinal sources covering history of the question. The scientific novelty lies in determination of correlation of the participation of interested parties in Internet governance. It is noted that in the practice of Internet governance there are two clear approaches for implementation of this model: ran by states and international organizations, or one that is ran by private entities. The international legal acts reflect the former approach towards implementation of this model. In accordance with this approach, the work on Internet governance in consultation of state with private entities, allowing private entities as observers, or creation of public-private partnerships aimed at solution of global issues. The leading role of the private sector is advanced by the United States and several other Western nations as an alternative to translational multilateral approach, which contradicts the international legal acts, since the role ICANN as a leader is delegated to private entities, rather than states.

The Conditions of Mercy

This article examines John Bunyan’s relationship to traditions of representing labor reaching back before the Reformation, from Piers Plowman and its imitators through to a range of “plowman” satires, complaints, and reformist dialogues in the sixteenth and seventeenth centuries. Bunyan’s provocative rejection of the virtues of labor in part one of The Pilgrim’s Progress brings together theological convictions about justification by faith with a vision of mechanic mobility and schooling in the Spirit that disrupts a range of social forms and hierarchies. Yet in part two of The Pilgrim’s Progress, particularly in the figure of Mercie, Bunyan offers up a new valuation of the exemplarist potentials of labor. Part two expands rather than contracts Bunyan’s exploration of the active life of dissent, reimagining questions of embodiment, habituation, imitation, and community. Mercie’s labors are performed in continuity with a late medieval tradition linking work and virtue. Her example prompts reconsideration both of Bunyan’s own dissenting allegories and of the uses of literary forms and ethical traditions across conventional period boundaries and confessional identities.

Women and Exilic Identity in the Hebrew Bible

Women and Exilic Identity in the Hebrew Bible

The Plasma Membrane-An Integrating Compartment for Mechano-Signaling.

Plants are able to sense their mechanical environment. This mechanical signal is used by the plant to determine its phenotypic features. This is true also at a smaller scale. Morphogenesis, both at the cell and tissue level, involves mechanical signals that influence specific patterns of gene expression and trigger signaling pathways. How a mechanical stress is perceived and how this signal is transduced into the cell remains a challenging question in the plant community. Among the structural components of plant cells, the plasma membrane has received very little attention. Yet, its position at the interface between the cell wall and the interior of the cell makes it a key factor at the nexus between biochemical and mechanical cues. So far, most of the key players that are described to perceive and maintain mechanical cell status and to respond to a mechanical stress are localized at or close to the plasma membrane. In this review, we will focus on the importance of the plasma membrane in mechano-sensing and try to illustrate how the composition of this dynamic compartment is involved in the regulatory processes of a cell to respond to mechanical stress.

Comparing Medical Term Usage Patterns of Professionals and Search Engine and Community Question Answering Service Users in Japan: Log Analysis.

BackgroundDespite increasing opportunities for acquiring health information online, discussion of the specific words used in searches has been limited.ObjectiveThe aim of this study was to clarify the medical information gap between medical professionals and the general public in Japan through health information–seeking activities on the internet.MethodsSearch and posting data were analyzed from one of the most popular domestic search engines in Japan (Yahoo! JAPAN Search) and the most popular Japanese community question answering service (Yahoo! Chiebukuro). We compared the frequency of 100 clinical words appearing in the clinical case reports of medical professionals (clinical frequency) with their frequency in Yahoo! JAPAN Search (search frequency) logs and questions posted to Yahoo! Chiebukuro (question frequency). The Spearman correlation coefficient was used to quantify association patterns among the three information sources. Additionally, user information (gender and age) in the search frequency associated with each registered user was extracted.ResultsSignificant correlations were observed between clinical and search frequencies (r=0.29, P=.003), clinical and question frequencies (r=0.34, P=.001), and search and question frequencies (r=0.57, P<.001). Low-frequency words in clinical frequency (eg, “hypothyroidism,” “ulcerative colitis”) highly ranked in search frequency. Similarly, “pain,” “slight fever,” and “numbness” were highly ranked only in question frequency. The weighted average of ages was 34.5 (SD 2.7) years, and the weighted average of gender (man –1, woman +1) was 0.1 (SD 0.1) in search frequency. Some words were specifically extracted from the search frequency of certain age groups, including “abdominal pain” (10-20 years), “plasma cells” and “inflammatory findings” (20-30 years), “DM” (diabetes mellitus; 30-40 years), “abnormal shadow” and “inflammatory findings” (40-50 years), “hypertension” and “abnormal shadow” (50-60 years), and “lung cancer” and “gastric cancer” (60-70 years).ConclusionsSearch and question frequencies showed similar tendencies, whereas search and clinical frequencies showed discrepancy. Low-clinical frequency words related to diseases such as “hypothyroidism” and “ulcerative colitis” had high search frequencies, whereas those related to symptoms such as “pain,” “slight fever,” and “numbness” had high question frequencies. Moreover, high search frequency words included designated intractable diseases such as “ulcerative colitis,” which has an incidence of less than 0.1% in the Japanese population. Therefore, it is generally worthwhile to pay attention not only to major diseases but also to minor diseases that users frequently seek information on, and more words will need to be analyzed in the future. Some characteristic words for certain age groups were observed (eg, 20-40 years: “cancer”; 40-60 years: diagnoses and diseases identified in health examinations; 60-70 years: diseases with late adulthood onset and “death”). Overall, this analysis demonstrates that medical professionals as information providers should be aware of clinical frequency, and medical information gaps between professionals and the general public should be bridged.

Attentive User-Engaged Adversarial Neural Network for Community Question Answering

We study the community question answering (CQA) problem that emerges with the advent of numerous community forums in the recent past. The task of finding appropriate answers to questions from informative but noisy crowdsourced answers is important yet challenging in practice. We present an Attentive User-engaged Adversarial Neural Network (AUANN), which interactively learns the context information of questions and answers, and enhances user engagement with the CQA task. A novel attentive mechanism is incorporated to model the semantic internal and external relations among questions, answers and user contexts. To handle the noise issue caused by introducing user context, we design a two-step denoise mechanism, including a coarse-grained selection process by similarity measurement, and a fine-grained selection process by applying an adversarial training module. We evaluate the proposed method on large-scale real-world datasets SemEval-2016 and SemEval-2017. Experimental results verify the benefits of incorporating user information, and show that our proposed model significantly outperforms the state-of-the-art methods.

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

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.

Coriander Genomics Database: a genomic, transcriptomic, and metabolic database for coriander

Coriander (Coriandrum sativum L.), also known as cilantro, is a globally important vegetable and spice crop. Its genome and that of carrot are models for studying the evolution of the Apiaceae family. Here, we developed the Coriander Genomics Database (CGDB, http://cgdb.bio2db.com/) to collect, store, and integrate the genomic, transcriptomic, metabolic, functional annotation, and repeat sequence data of coriander and carrot to serve as a central online platform for Apiaceae and other related plants. Using these data sets in the CGDB, we intriguingly found that seven transcription factor (TF) families showed significantly greater numbers of members in the coriander genome than in the carrot genome. The highest ratio of the numbers of MADS TFs between coriander and carrot reached 3.15, followed by those for tubby protein (TUB) and heat shock factors. As a demonstration of CGDB applications, we identified 17 TUB family genes and conducted systematic comparative and evolutionary analyses. RNA-seq data deposited in the CGDB also suggest dose compensation effects of gene expression in coriander. CGDB allows bulk downloading, significance searches, genome browser analyses, and BLAST searches for comparisons between coriander and other plants regarding genomics, gene families, gene collinearity, gene expression, and the metabolome. A detailed user manual and contact information are also available to provide support to the scientific research community and address scientific questions. CGDB will be continuously updated, and new data will be integrated for comparative and functional genomic analysis in Apiaceae and other related plants.

Mining shape of expertise: A novel approach based on convolutional neural network

Expert finding addresses the task of retrieving and ranking talented people on the subject of user query. It is a practical issue in the Community Question Answering networks. Recruiters looking for knowledgeable people for their job positions are the most important clients of expert finding systems. In addition to employee expertise, the cost of hiring new staff is another significant concern for organizations. An efficient solution to cope with this concern is to hire T-shaped experts that are cost-effective. In this study, we have proposed a new deep model for T-shaped experts finding based on Convolutional Neural Networks. The proposed model tries to match queries and users by extracting local and position-invariant features from their corresponding documents. In other words, it detects users’ shape of expertise by learning patterns from documents of users and queries simultaneously. The proposed model contains two parallel CNN’s that extract latent vectors of users and queries based on their corresponding documents and join them together in the last layer to match queries with users. Experiments on a large subset of Stack Overflow documents indicate the effectiveness of the proposed method against baselines in terms of NDCG, MRR, and ERR evaluation metrics.