Nowadays, many platforms on the Web offer organized events, allowing users to
be organizers or participants. For such platforms, it is beneficial to predict
potential event participants. Existing work on this problem tends to borrow
recommendation techniques. However, compared to e-commerce items and purchases,
events and participation are usually of a much smaller frequency, and the data
may be insufficient to learn an accurate model. In this paper, we propose to
utilize social media retweeting activity data to enhance the learning of event
participant prediction models. We create a joint knowledge graph to bridge the
social media and the target domain, assuming that event descriptions and tweets
are written in the same language. Furthermore, we propose a learning model that
utilizes retweeting information for the target domain prediction more
effectively. We conduct comprehensive experiments in two scenarios with
real-world data. In each scenario, we set up training data of different sizes,
as well as warm and cold test cases. The evaluation results show that our
approach consistently outperforms several baseline models, especially with the
warm test cases, and when target domain data is limited.

利用社交媒体转发活动数据增强事件参与者预测模型的学习，在两种真实环境下对训练数据以及热启动和冷启动测试情景进行综合实验，评估结果表明我们的方法在几个基准模型中始终表现优异，尤其在限定目标领域数据时。

社交媒体转发数据增强的组织活动参与者预测

Organized Event Participant Prediction Enhanced by Social Media  Retweeting Data

Emotion classification in NLP assigns emotions to texts, such as sentences or
paragraphs. With texts like "I felt guilty when he cried", focusing on the
sentence level disregards the standpoint of each participant in the situation:
the writer ("I") and the other entity ("he") could in fact have different
affective states. The emotions of different entities have been considered only
partially in emotion semantic role labeling, a task that relates semantic roles
to emotion cue words. Proposing a related task, we narrow the focus on the
experiencers of events, and assign an emotion (if any holds) to each of them.
To this end, we represent each emotion both categorically and with appraisal
variables, as a psychological access to explaining why a person develops a
particular emotion. On an event description corpus, our experiencer-aware
models of emotions and appraisals outperform the experiencer-agnostic
baselines, showing that disregarding event participants is an
oversimplification for the emotion detection task.

通过引入经验者概念，将情感标签分配给相关事件中的感受者，通过此方法，作者证明了在情感检测中忽略事件参与者是一种简化，但在情感检测任务中，忽略事件参与者是一种简化，但使用考虑事件参与者和评估的经验者感知模型来对情感进行分类比基于经验者的模型更为准确。