AliCoCo: Alibaba E-commerce Cognitive Concept Net
Table 4. Experimental results in shopping concept generation.
['Model', 'Precision']
Comparing to the baseline, which is a base BiLSTM with self attention architecture, adding wide features such as different syntactic features of concept improves the precision by 3\% in absolute value. When we replace the input embedding with BERT output, the performance improves another 1.5\%, which shows the advantage of rich semantic information encoded by BERT. After introducing external knowledge into our model, the final performance reaches to 0.935, improving by a relative gain of 7.5\% against the baseline model, indicating that leveraging external knowledge benefits commonsense reasoning on short concepts.