An intensive recent research work investigated the combined use of hand-curated knowledge resources and corpus-driven resources to learn effective text representations. The overall learning process could be run by online revising the learning objective or by offline refining an original learned representation. The differentiated impact of each of the learning approaches on the quality of the learned representations has not been studied so far in the literature. This article focuses on the design of comparable offline vs. online knowledge-enhanced document representation learning models and the comparison of their effectiveness using a set of standard IR and NLP downstream tasks. The results of quantitative and qualitative analyses show that (1) offline vs. online learning approaches have dissimilar result trends regarding the task as well as the dataset distribution counts with regard to domain application; (2) while considering external knowledge resources is undoubtedly beneficial, the way used to express relational constraints could affect semantic inference effectiveness. The findings of this work present opportunities for the design of future representation learning models, but also for providing insights about the evaluation of such models.

In this paper, we specifically consider the challenging task of solving a question posted on Twitter. The latter generally remains unanswered and most of the replies, if any, are only from members of the questioner's neighborhood. As outlined in previous work related to community Q&A, we believe that question-answering is a collaborative process and that the relevant answer to a question post is an aggregation of answer nuggets posted by a group of relevant users. Thus, the problem of identifying the relevant answer turns into the problem of identifying the right group of users who would provide useful answers and would possibly be willing to collaborate together in the long-term. Accordingly, we present a novel method, called CRAQ, that is built on the collaboration paradigm and formulated as a group entropy optimization problem. To optimize the quality of the group, an information gain measure is used to select the most likely ``informative" users according to topical and collaboration likelihood predictive features. Crowd-based experiments performed on two crisis-related Twitter datasets demonstrate the effectiveness of our collaborative-based answering approach.