Effective Phrase Prediction

Autocompletion is a widely deployed facility in systems that require user input. Having the system complete a partially typed "word" can save user time and effort. In this paper, we study the problem of autocompletion not just at the level of a single "word", but at the level of a multi-word "phrase". There are two main challenges: one is that the number of phrases (both the number possible and the number actually observed in a corpus) is combinatorially larger than the number of words; the second is that a "phrase", unlike a "word", does not have a well-defined boundary, so that the autocompletion system has to decide not just what to predict, but also how far. We introduce a FussyTree structure to address the first challenge and the concept of a significant phrase to address the second. We develop a probabilistically driven multiple completion choice model, and exploit features such as frequency distributions to improve the quality o...

• Type: Conference
• Year: 2007
• Where: VLDB
• Authors: Arnab Nandi, H. V. Jagadish
• Keywords: Database, VLDB 2007