Challenges in the Knowledge Base Population Slot Filling Task

摘要

The Knowledge Based Population (KBP) evaluation track of the Text Analysis Conferences (TAC) has been held for the past 3 years. One of the two tasks of KBP is slot filling: finding within a large corpus the values of a set of attributes of given people and organizations. This task has proven very challenging, with top systems rarely exceeding 30% F-measure. In this paper, we present an error analysis and classification for those answers which could be found by a manual corpus search but were not found by any of the systems participating in the 2010 evaluation. The most common sources of failure were limitations on inference, errors in coreference (particularly with nominal anaphors), and errors in named entity recognition. We relate the types of errors to the characteristics of the task and show the wide diversity of problems that must be addressed to improve overall performance.