Augmenting Input Method Language Model with user Location Type Information

Abstract: Geo-tags from micro-blog posts have been shown to be useful in many data mining applications. This work seeks to find out if the location type derived from these geo-tags can benefit input methods, which attempts to predict the next word a user will input during typing. If a correlation between different location types and a change in word distribution can be found, the location type information can be used to make the input method more accurate. This work queried micro-blog posts from Twitter API and location type of these posts from Google Place API, forming a dataset of around 500k samples. A statistical study on the word distribution found weak support for the assumption. An LSTM based prediction experiment found a 2% edge in the accuracy from LLMs leveraging location type information when compared to a baseline without that information.