159,971 research outputs found
Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A Platforms
Question categorization and expert retrieval methods have been crucial for
information organization and accessibility in community question & answering
(CQA) platforms. Research in this area, however, has dealt with only the text
modality. With the increasing multimodal nature of web content, we focus on
extending these methods for CQA questions accompanied by images. Specifically,
we leverage the success of representation learning for text and images in the
visual question answering (VQA) domain, and adapt the underlying concept and
architecture for automated category classification and expert retrieval on
image-based questions posted on Yahoo! Chiebukuro, the Japanese counterpart of
Yahoo! Answers.
To the best of our knowledge, this is the first work to tackle the
multimodality challenge in CQA, and to adapt VQA models for tasks on a more
ecologically valid source of visual questions. Our analysis of the differences
between visual QA and community QA data drives our proposal of novel
augmentations of an attention method tailored for CQA, and use of auxiliary
tasks for learning better grounding features. Our final model markedly
outperforms the text-only and VQA model baselines for both tasks of
classification and expert retrieval on real-world multimodal CQA data.Comment: Submitted for review at CIKM 201
Language Use Matters: Analysis of the Linguistic Structure of Question Texts Can Characterize Answerability in Quora
Quora is one of the most popular community Q&A sites of recent times.
However, many question posts on this Q&A site often do not get answered. In
this paper, we quantify various linguistic activities that discriminates an
answered question from an unanswered one. Our central finding is that the way
users use language while writing the question text can be a very effective
means to characterize answerability. This characterization helps us to predict
early if a question remaining unanswered for a specific time period t will
eventually be answered or not and achieve an accuracy of 76.26% (t = 1 month)
and 68.33% (t = 3 months). Notably, features representing the language use
patterns of the users are most discriminative and alone account for an accuracy
of 74.18%. We also compare our method with some of the similar works (Dror et
al., Yang et al.) achieving a maximum improvement of ~39% in terms of accuracy.Comment: 1 figure, 3 tables, ICWSM 2017 as poste
QDEE: Question Difficulty and Expertise Estimation in Community Question Answering Sites
In this paper, we present a framework for Question Difficulty and Expertise
Estimation (QDEE) in Community Question Answering sites (CQAs) such as Yahoo!
Answers and Stack Overflow, which tackles a fundamental challenge in
crowdsourcing: how to appropriately route and assign questions to users with
the suitable expertise. This problem domain has been the subject of much
research and includes both language-agnostic as well as language conscious
solutions. We bring to bear a key language-agnostic insight: that users gain
expertise and therefore tend to ask as well as answer more difficult questions
over time. We use this insight within the popular competition (directed) graph
model to estimate question difficulty and user expertise by identifying key
hierarchical structure within said model. An important and novel contribution
here is the application of "social agony" to this problem domain. Difficulty
levels of newly posted questions (the cold-start problem) are estimated by
using our QDEE framework and additional textual features. We also propose a
model to route newly posted questions to appropriate users based on the
difficulty level of the question and the expertise of the user. Extensive
experiments on real world CQAs such as Yahoo! Answers and Stack Overflow data
demonstrate the improved efficacy of our approach over contemporary
state-of-the-art models. The QDEE framework also allows us to characterize user
expertise in novel ways by identifying interesting patterns and roles played by
different users in such CQAs.Comment: Accepted in the Proceedings of the 12th International AAAI Conference
on Web and Social Media (ICWSM 2018). June 2018. Stanford, CA, US
Entity Ranking on Graphs: Studies on Expert Finding
Todays web search engines try to offer services for finding various information in addition to simple web pages, like showing locations or answering simple fact queries. Understanding the association of named entities and documents is one of the key steps towards such semantic search tasks. This paper addresses the ranking of entities and models it in a graph-based relevance propagation framework. In particular we study the problem of expert finding as an example of an entity ranking task. Entity containment graphs are introduced that represent the relationship between text fragments on the one hand and their contained entities on the other hand. The paper shows how these graphs can be used to propagate relevance information from the pre-ranked text fragments to their entities. We use this propagation framework to model existing approaches to expert finding based on the entity's indegree and extend them by recursive relevance propagation based on a probabilistic random walk over the entity containment graphs. Experiments on the TREC expert search task compare the retrieval performance of the different graph and propagation models
Supporting Answerers with Feedback in Social Q&A
Prior research has examined the use of Social Question and Answer (Q&A)
websites for answer and help seeking. However, the potential for these websites
to support domain learning has not yet been realized. Helping users write
effective answers can be beneficial for subject area learning for both
answerers and the recipients of answers. In this study, we examine the utility
of crowdsourced, criteria-based feedback for answerers on a student-centered
Q&A website, Brainly.com. In an experiment with 55 users, we compared
perceptions of the current rating system against two feedback designs with
explicit criteria (Appropriate, Understandable, and Generalizable). Contrary to
our hypotheses, answerers disagreed with and rejected the criteria-based
feedback. Although the criteria aligned with answerers' goals, and crowdsourced
ratings were found to be objectively accurate, the norms and expectations for
answers on Brainly conflicted with our design. We conclude with implications
for the design of feedback in social Q&A.Comment: Published in Proceedings of the Fifth Annual ACM Conference on
Learning at Scale, Article No. 10, London, United Kingdom. June 26 - 28, 201
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