Integrating Methods for Mining and Summarizing Text Conversations

由于互联网革命，大量个人和组织不断地使用电子邮件 交流、博客、短信和其他社交媒体活动。与此同时，语音方面的改进 技术使许多口头对话(例如，面对面的会议)能够被自动转录。这些趋势的最终结果是，人类对话数据--以书面形式--正在以惊人的速度积累。尽管已经提出了几种计算方法来分析、挖掘和总结这些非正式的对话文档，但它们在四个关键方面受到了限制。首先，大多数关于会话摘要的研究都集中在提取系统上，其中摘要只是输入会话中话语的一个子集。其次，大多数拟议的方法主要是针对会议和电子邮件开发的，对其他对话方式的工作很少。第三，这些方法大多是有监督的，需要在标注的语料库上进行训练。最后，当前的会话挖掘技术是非常特定于任务的；例如，用于主题建模、对话行为建模和提取会话结构的方法在很大程度上是独立开发的。 在这项提案中，我们计划通过制定以下措施来解决所有这些限制： -摘要系统将越来越抽象，更密切地反映人们自然产生和期望的东西。 -将数据和见解从一种对话方式转移到另一种对话方式的方法；例如，从会议和电子邮件到博客和微博。 -半监督和无监督机器学习技术，通过利用大量随时可用的未标记对话数据，可以轻松地应用于博客和推文。 -集成不同文本挖掘任务的框架。特别是，我们将研究对话框如何工作 建模、主题建模和情感分析可以同时、相互依赖地执行。

Due to the Internet Revolution, large numbers of individuals and organizations constantly engage in email exchanges, blogging, texting and other social media activities. At the same time, improvements in speech technology enable many spoken conversations (e.g., face -to-face meetings) to be automatically transcribed. The net result of these trends is that human conversational data-in written forms-are accumulating at a phenomenal rate. Although several computational methods have been proposed to analyze, mine and summarize these informal, conversational documents, they have been limited in four key ways. First, the majority of research on summarizing conversations has been focusing on extractive systems, where a summary is simply a subset of the utterances in the input conversation. Second, most of the proposed methods have been mainly developed for meetings and emails, with little work on other conversational modalities. Third, most of these methods are supervised ones, which need to be trained on annotated corpora. Finally, current conversation mining techniques are very task-specific; for instance methods for topic modeling, for dialog act modeling and for extracting the conversational structure have been developed largely independently. In this proposal, we plan to address all these limitations by developing: - Summarization systems that will be increasingly abstractive, reflecting more closely what people naturally produce and expect. - Methods to transfer data and insights from one conversational modality to another; for instance, from meetings and emails to blogs and micro-blogs. -Semi-supervised and unsupervised machine learning techniques that can be easily applied to blogs and tweets by leveraging large amounts of readily available unlabeled conversational data. -A framework for integrating the different text mining tasks. In particular, we will study how dialog act modeling, topic modeling and sentiment analysis can be performed simultaneously and interdependently.