Investigating how well contextual features are captured by bi-directional recurrent neural network models
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['[BOLD] Word', '[BOLD] Score']
At a sentence level, we only consider our best performing model, LSTM. It uses a sentence from CoNLL dataset - “Saturday ’s national congress of the ruling Czech (I-ORG) Civic (I-ORG) Democratic (I-ORG) Party ( I-ORG) ODS (I-ORG)) will discuss making the party more efficient and transparent , Foreign Minister and ODS (I-ORG) vice-chairman Josef (I-PER) Zieleniec (I-PER), said on Friday .”. The tags for all entity words are mentioned alongside each word. Notice the high scores for “vice-chairman”, “ruling”, “congress”, “minister” meets the intuitive understanding of these words. Interestingly, round brackets get the maximum scores for M_SLL method, which may be attributed to their frequent use with ORG entity words. Similarly, sentence taken from JNLPBA dataset is: “the number of glucocorticoid (B-protein) receptor (I-protein) sites in lymphocytes (B-cell_type) and plasma cortisol concentrations were measured in dgdg patients who had recovered from major depressive disorder and dgdg healthy control subjects .”. Again, higher scores for “sites” and “plasma” for cell_type are in agreement with overall scores given to them.