Investigating how well contextual features are captured by bi-directional recurrent neural network models
Table 4: Relevance scores for the word “minister” in three different test sentences from CoNLL dataset.
['[BOLD] RNN', '[BOLD] LSTM', '[BOLD] GRU', '[BOLD] Sentence']
In this section, we analyze how the position of context words affects their scores obtained by M_LRC method. We do this analysis for real sentences present in the test sets as well as on artificial sentences. We achieve this by applying the proposed techniques at an individual sentence level. M_WF method indicates that “minister” has high importance for entity type PER matching with our intuition. In the first sentence, there is no entity word for PER, hence, the score for “minister”, corresponding to entity PER is zero. In the second sentence, the score is higher, though not too high as the word is relatively far from the relevant entity word. However, the score is much higher in the third sentence where “minister” is right before the entity words “Margaret Thatcher”. Relative scores obtained by using different neural models also match with the general notion that RNN tends to forget long range context (second sentence) compared to LSTM and GRU, and is quite good for short distance context (third sentence).