3 research outputs found
Of Wines and Reviews: Measuring and Modeling the Vivino Wine Social Network
This paper presents an analysis of social experiences around wine consumption
through the lens of Vivino, a social network for wine enthusiasts with over 26
million users worldwide. We compare users' perceptions of various wine types
and regional styles across both New and Old World wines, examining them across
price ranges, vintages, regions, varietals, and blends. Among other things, we
find that ratings provided by Vivino users are not biased by cost. We then
study how wine characteristics, language in wine reviews, and the distribution
of wine ratings can be combined to develop prediction models. More
specifically, we model user behavior to develop a regression model for
predicting wine ratings, and a classifier for determining user review
preferences.Comment: A preliminary version of this paper appears in the Proceedings of the
IEEE/ACM International Conference on Advances in Social Networks Analysis and
Mining (ASONAM 2018). This is the full versio
Policy Compliance Detection via Expression Tree Inference
Policy Compliance Detection (PCD) is a task we encounter when reasoning over
texts, e.g. legal frameworks. Previous work to address PCD relies heavily on
modeling the task as a special case of Recognizing Textual Entailment.
Entailment is applicable to the problem of PCD, however viewing the policy as a
single proposition, as opposed to multiple interlinked propositions, yields
poor performance and lacks explainability. To address this challenge, more
recent proposals for PCD have argued for decomposing policies into expression
trees consisting of questions connected with logic operators. Question
answering is used to obtain answers to these questions with respect to a
scenario. Finally, the expression tree is evaluated in order to arrive at an
overall solution. However, this work assumes expression trees are provided by
experts, thus limiting its applicability to new policies. In this work, we
learn how to infer expression trees automatically from policy texts. We ensure
the validity of the inferred trees by introducing constrained decoding using a
finite state automaton to ensure the generation of valid trees. We determine
through automatic evaluation that 63% of the expression trees generated by our
constrained generation model are logically equivalent to gold trees. Human
evaluation shows that 88% of trees generated by our model are correct