Focus paper: Characterizing Microblogs with Topic Models
Related paper: Unsupervised Modeling of Twitter Conversations; Alan Ritter, Colin Cherry, Bill Dolan, NAACL 2010
The paper proposes an unsupervised approach to task of modeling dialogue acts. It is based on conversation model by Barzilay and Lee (2004) using HMMs for topic transitions, with each topic generating a message. Another latent variable - source is added that generates a particular word. A source may be one of (1) The current post's dialogue act, (2) Conversation's topic and (3) General English. Evalation is done by comparing the probability of the held out test data using Chib's estimator. Training is performed on a set of 10000 randomly sampled conversations with 3-6 posts. An interpretation of the model based on the 10-act dialogue model is presented. I observed that their interpretation presents a transition graph which is acyclic (excluding the self loops). This probably could be because of the length of the conversations and also that transitions with probability less than 0.1 are not shown. They are however not discussed. A comparison metric based on the ordering of the posts is proposed. The posts for a particular conversation are permuted and the Kendall co-efficient is calculated.