We examine a large dialog corpus obtained from the conversation history of a single individual with 104 conversation partners. The corpus consists of half a million instant messages, across several messaging platforms. We focus our analyses on seven speaker attributes, each of which partitions the set of speakers, namely: gender; relative age; family member; romantic partner; classmate; co-worker; and native to the same country. In addition to the content of the messages, we examine conversational aspects such as the time messages are sent, messaging frequency, psycholinguistic word categories, linguistic mirroring, and graph-based features reflecting how people in the corpus mention each other. We present two sets of experiments predicting each attribute using (1) short context windows; and (2) a larger set of messages. We find that using all features leads to gains of 9-14% over using message text only.

我们研究了一个大型对话语料库，通过单个个体与104个对话伙伴的对话历史记录获得。除了探讨消息文本外，我们还研究了对话方面的各种特征，如发送消息的时间、消息频率、心理语言学词类、语言镜像以及反映语料库中人们如何提及对方的基于图形的特征，并使用两组实验来预测每个属性，得出使用全部特征相对于仅使用消息文本可提高9-14％的结论。

从个人长期对话中推断讲话者属性