Simple Question Answering by Attentive Convolutional Neural Network This work was conducted during the first author’s internship at IBM Watson Group.
Table 2: Experimental results for entity linking
['[BOLD] N', '[BOLD] baseline  [BOLD] raw', '[BOLD] baseline  [BOLD] rerank', '[BOLD] Ours  [BOLD] passive-linker', '[BOLD] Ours  [BOLD] -a', '[BOLD] Ours  [BOLD] -b', '[BOLD] Ours  [BOLD] -c', '[BOLD] Ours  [BOLD] active-linker']
\newcitegolub2016character report the coverage of ground truth by top-N cases (N∈{1,10,20,50,100}). In addition, they explore a reranking algorithm to refine the entity ranking list. (-a, -b, -c). Our passive entity linker outperforms the baseline’s raw results by big margins and is 2–3 percent above their reranked scores. This shows the outstanding performance of our passive entity linker despite its simplicity. The table also shows that all three factors (a, b, c) matter. Observations: (i) Each factor matters more when N is smaller. This makes sense because when N reaches the entity vocabulary size, all methods will have coverage 100%. (ii) The position-related factor c has less influence. From top1 to top100, its contribution decreases from 4.3 to .9. Our linker still outperforms the reranked baseline for N≥20. (iii) Factor a is dominant for small N, presumably because it chooses the longer one when two candidates exist, which is critical for small N. (iv) Factor b plays a more consistent role across different N. We release our entity linking results for follow-up work to make better comparison.