We describe a generative probabilistic model of natural language, which we call HBG, that takes advantage of detailed linguistic information to resolve ambiguity. HBG incorporates...
Ezra Black, Frederick Jelinek, John D. Lafferty, D...
Abstract. We are developing a methodology of Test-Driven Development of Models (TDDM) based on an experimental UML2.0 modeling tool SMART. Our experience shows that TDDM is quite u...
Susumu Hayashi, Pan YiBing, Masami Sato, Kenji Mor...
We present a game-based interface for acquiring common sense knowledge. In addition to being interactive and entertaining, our interface guides the knowledge acquisition process t...
Robert Speer, Jayant Krishnamurthy, Catherine Hava...
Traditionally, machine learning approaches for information extraction require human annotated data that can be costly and time-consuming to produce. However, in many cases, there ...
We analyze a massive social network, gathered from the records of a large mobile phone operator, with more than a million users and tens of millions of calls. We examine the distr...