论文标题
合作组织小组聊天中的答案标识
Answer Identification in Collaborative Organizational Group Chat
论文作者
论文摘要
我们提出了一种简单的无监督方法，可以在组织组聊天中进行答案识别。近年来，组织团体聊天正在上升，可以在不同位置和时区的同事之间进行基于文本的合作。寻找问题的答案通常对于工作效率至关重要。但是，小组聊天的特征是交织在一起的对话和“始终”可用性，这使用户很难查明对他们实时关心的问题的答案，或在回顾性中搜索答案。此外，聊天组之间的结构和词汇特征有所不同，因此很难找到“一个模型适合所有模型”方法。我们的内核密度估计（KDE）基于聚类方法被称为ANS-CHAT隐式学习讨论模式作为答案识别的手段，从而消除了对渠道特定标记的需求。经验评估表明，该解决方案的表现优于其他方法。
We present a simple unsupervised approach for answer identification in organizational group chat. In recent years, organizational group chat is on the rise enabling asynchronous text-based collaboration between co-workers in different locations and time zones. Finding answers to questions is often critical for work efficiency. However, group chat is characterized by intertwined conversations and 'always on' availability, making it hard for users to pinpoint answers to questions they care about in real-time or search for answers in retrospective. In addition, structural and lexical characteristics differ between chat groups, making it hard to find a 'one model fits all' approach. Our Kernel Density Estimation (KDE) based clustering approach termed Ans-Chat implicitly learns discussion patterns as a means for answer identification, thus eliminating the need to channel-specific tagging. Empirical evaluation shows that this solution outperforms other approached.
