In recent years, sentiment analysis in social media has attracted a lot of
research interest and has been used for a number of applications.
Unfortunately, research has been hindered by the lack of suitable datasets,
complicating the comparison between approaches. To address this issue, we have
proposed SemEval-2013 Task 2: Sentiment Analysis in Twitter, which included two
subtasks: A, an expression-level subtask, and B, a message-level subtask. We
used crowdsourcing on Amazon Mechanical Turk to label a large Twitter training
dataset along with additional test sets of Twitter and SMS messages for both
subtasks. All datasets used in the evaluation are released to the research
community. The task attracted significant interest and a total of 149
submissions from 44 teams. The best-performing team achieved an F1 of 88.9% and
69% for subtasks A and B, respectively.

通过提出 SemEval-2013 任务 2：Twitter 情感分析的表达级子任务和消息级子任务以及在亚马逊机械土耳其上使用众包技术标记大型 Twitter 训练数据集及其他的测试集，对情感分析在社交媒体上进行了研究。该研究因结果的准确性获得了广泛关注和讨论。

SemEval-2013 任务 2：Twitter 中的情感分析

SemEval-2013 Task 2: Sentiment Analysis in Twitter

In this paper, we describe SemEval-2013 Task 4: the definition, the data, the
evaluation and the results. The task is to capture some of the meaning of
English noun compounds via paraphrasing. Given a two-word noun compound, the
participating system is asked to produce an explicitly ranked list of its
free-form paraphrases. The list is automatically compared and evaluated against
a similarly ranked list of paraphrases proposed by human annotators, recruited
and managed through Amazon's Mechanical Turk. The comparison of raw paraphrases
is sensitive to syntactic and morphological variation. The "gold" ranking is
based on the relative popularity of paraphrases among annotators. To make the
ranking more reliable, highly similar paraphrases are grouped, so as to
downplay superficial differences in syntax and morphology. Three systems
participated in the task. They all beat a simple baseline on one of the two
evaluation measures, but not on both measures. This shows that the task is
difficult.

本文介绍了 SemEval-2013 任务 4 的定义、数据、评估和结果，并描述了该任务的目标是通过解释性改写捕捉英语名词复合词的一些意义。三个系统参加了任务，他们在其中一个评估措施上击败了简单基线，但在两个评估措施上都没有击败它。

SemEval-2013 任务 4：名词复合词的自由同义重组

SemEval-2013 Task 4: Free Paraphrases of Noun Compounds

We describe the TempEval-3 task which is currently in preparation for the
SemEval-2013 evaluation exercise. The aim of TempEval is to advance research on
temporal information processing. TempEval-3 follows on from previous TempEval
events, incorporating: a three-part task structure covering event, temporal
expression and temporal relation extraction; a larger dataset; and single
overall task quality scores.

描述了即将在 SemEval-2013 评估活动中准备的 TempEval-3 任务，旨在推进关于时间信息处理的研究，包括一个三部分任务结构，涵盖事件、时间表达和时间关系提取，一个更大的数据集和单一的总体任务质量得分。