Intension classification of user queries in intelligent customer service system

Abstract

In this paper, we investigate the popular neural word embedding method word2vec as a source of evidence in short text similarity calculation, which has been applied to intention classification of user queries in our intelligent customer service system — Alime. For avoiding information loss on sentence-level vector representation, we design a Word Similarity Maximization (WSM) method, which utilize word-level embedding vectors to directly calculate query similarity. Experimental results on a dataset from real daily user query data from Alime system show WSM can better detect query-intention relation than baseline methods.