Classifying Dialogue Acts in One-on-One Live Chats
نویسندگان
چکیده
We explore the task of automatically classifying dialogue acts in 1-on-1 online chat forums, an increasingly popular means of providing customer service. In particular, we investigate the effectiveness of various features and machine learners for this task. While a simple bag-of-words approach provides a solid baseline, we find that adding information from dialogue structure and inter-utterance dependency provides some increase in performance; learners that account for sequential dependencies (CRFs) show the best performance. We report our results from testing using a corpus of chat dialogues derived from online shopping customer-feedback data.
منابع مشابه
Extracting Keywords from Multi-party Live Chats
Live chats have become a popular form of communication, connecting people all over the globe. We believe that one of the simplest approaches for providing topic information to users joining a chat is keywords. In this paper, we present a method to automatically extract contextually relevant keywords for multi-party live chats. In our work, we identify keywords that are associated with specific ...
متن کاملClassifying Dialogue Acts in Multi-party Live Chats
We consider the task of classifying chat contributions by dialogue act in a multi-party setting. This extends the problem significantly over the 1-1 chat scenario due to the semiasynchronous and “entangled” nature of the contributions by chat participants. We experiment with a number of machine learning approaches, using different categories of features: lexical, contextual, structural, keyword...
متن کامل(Dis)agreements in Iranians’ Internet Relay Chats
The present study on politeness is an attempt to examine (dis)agreeing strategies utilized by EFL learners while chatting on the internet. Subjects of the study were forty male and thirty-three female Iranian natives whose internet relay chat (IRC) interactions, composed of 400 excerpts, were collected between December 2007 and September 2008. Data analysis was based on the general taxonomy of ...
متن کاملUnsupervised Classification of Student Dialogue Acts with Query-Likelihood Clustering
Dialogue acts model the intent underlying dialogue moves. In natural language tutorial dialogue, student dialogue moves hold important information about knowledge and goals, and are therefore an integral part of providing adaptive tutoring. Automatically classifying these dialogue acts is a challenging task, traditionally addressed with supervised classification techniques requiring substantial...
متن کاملExtractive Summarization and Dialogue Act Modeling on Email Threads: An Integrated Probabilistic Approach
In this paper, we present a novel supervised approach to the problem of summarizing email conversations and modeling dialogue acts. We assume that there is a relationship between dialogue acts and important sentences. Based on this assumption, we introduce a sequential graphical model approach which simultaneously summarizes email conversation and models dialogue acts. We compare our model with...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2010