Language is being increasingly harnessed to not only create natural human-machine interfaces but also to infer social behaviors and interactions. In the same vein, we investigate a novel spoken language task, of inferring social relationships in two-party conversations: whether the two parties are related as family, strangers or are involved in business transactions. For our study, we created a corpus of all incoming and outgoing calls from a few homes over the span of a year. On this unique naturalistic corpus of everyday telephone conversations, which is unlike Switchboard or any other public domain corpora, we demonstrate that standard natural language processing techniques can achieve accuracies of about 88%, 82%, 74% and 80% in differentiating business from personal calls, family from non-family calls, familiar from unfamiliar calls and family from other personal calls respectively. Through a series of experiments with our classifiers, we characterize the properties of telephone conversations and find: (a) that 30 words of openings (beginnings) are sufficient to predict business from personal calls, which could potentially be exploited in designing context sensitive interfaces in smart phones; (b) our corpus-based analysis does not support Schegloff and Sack's manual analysis of exemplars in which they conclude that pre-closings differ significantly between business and personal calls - closing fared no better than a random segment; and (c) the distribution of different types of calls are stable over durations as short as 1-2 months. In summary, our results show that social relationships can be inferred automatically in two-party conversations with sufficient accuracy to support practical applications.

中文翻译：

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从单词推断对话的社会性质：对日常电话对话语料库的实验。

语言不仅被越来越多地用于创建自然的人机界面，而且还被用于推断社会行为和交互。同样，我们研究了一项新颖的口语任务，即推断两方对话中的社会关系：两方是家人、陌生人还是涉及商业交易。在我们的研究中，我们创建了一个包含一年中来自几个家庭的所有来电和去电的语料库。在这个独特的日常电话对话自然语料库中，不同于 Switchboard 或任何其他公共领域的语料库，我们证明标准的自然语言处理技术可以实现约 88%、82%、74% 和 80% 的准确率，以区分业务与个人来自非家庭电话的家庭电话，熟悉的电话分别来自陌生的电话和家人的电话。通过对我们的分类器进行的一系列实验，我们表征了电话交谈的特性，并发现：(a) 30 个开头（开头）词足以从个人电话中预测业务，这可能会被用于设计上下文相关的界面智能手机; (b) 我们基于语料库的分析不支持 Schegloff 和 Sack 对示例的手动分析，他们得出的结论是，商务电话和个人电话之间的预结算差异很大 - 结算的表现并不比随机部分好；(c) 不同类型呼叫的分布在短至 1-2 个月的持续时间内保持稳定。总之，