Evaluating the word-expert approach for Named-Entity Disambiguation
Table 10: Performance of dictionaries on the news subset of the development dataset — as micro-averages — for two individual dictionaries (EXCT and GOOG), the two dictionary cascades (LNRM and FUZZ) and our heuristic dictionary (HEUR). For LNRM, we show also results with partitions of counts (i.e., just the popularity within Wikipedia versus on the rest of the web).
['EXCT', '0.6937']
Results from GOOG are competitive with cascades and may be a good alternative in situations when full dictionaries are unavailable. Although performance of top candidates is indicative of a dictionary’s overall quality, it tells us little about the less popular choices. We use dictionaries to expand queries into pools of possible candidates, to be disambiguated by a supervised system. Therefore, it is important to understand how close a dictionary might come to capturing all — even low-ranking — entities that may be relevant to a gold standard.