Cross-Domain Intention Detection in Discussion Forums
Ngo Xuan Bach, Le Cong Linh, Tu Minh Phuong · 2017
This paper presents a method for cross-domain intention detection, which identifies posts expressing user intentions in discussion forums. The results of the task can be beneficial for intelligent business such as online advertising and marketing. Our method utilizes labeled data from several other domains to help the learning task in the target domain using a Naive Bayesian framework. Since the distributions of data vary from domain to domain, it is important to adjust the contributions of different data sources in the learning model to achieve high accuracy. Here, we propose to optimize the parameters of the Naive Bayes classifier using a stochastic gradient descent (SGD) algorithm. Experimental results show that our method outperforms several competitive baselines on a benchmark dataset, consisting of forum posts in four domains, namely Cellphone, Electronics, Camera, and TV.