Fine-Grained Entity Typing for Domain Independent Entity Linking
Table 3: Accuracy on the CoNLL development set (testa) with different amounts of context fed to our model. Adding the first sentence of the document gives the best performance because this is often indicative of topic in this dataset (e.g., what sport is being discussed).
['Amount of Context', 'Dev']
S6SS1SSS0Px1 How much context information should we add? On the CoNLL dataset, sentences containing entity mentions are often quite short, but are embedded in a larger document. We investigate the most effective amount of context information to add to our typing model. We test the context sentence only, the left and right 50 tokens of the mention span, and the first sentence of the document. Adding the left and right 50 tokens of the mention span improves the accuracy over the context sentence only. Adding the first sentence of the document improves the accuracy over the context sentence only (no additional context) by 4 points. This is especially useful when the document is a list of sports results, and a sentence does not have rich context. For example, one sentence is “Michael Johnson ( U.S. ) 20.02”, which is highly uninformative, but the first sentence of the document is “ATHLETICS - BERLIN GRAND PRIX RESULTS.” Our model correctly predicts Michael_Johnson_(sprinter) after giving more context information about the sport.