Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined RepresentationsM.A. ALi and W. Wang are the co-corresponding authors.
Table 4: Phase-I vs Phase (I-II) comparison for BBN data
['Labels', 'Support', 'Prec', 'Phase-I Rec', 'F1', 'Prec', 'Phase I-II Rec', 'F1']
The major reason for poor performance in Phase-I is highly correlated nature of entity types with substantial contextual overlap. The distant supervision, moreover, adds to the intricacy of the problem. For example, “organization” and “cooperation” are two highly correlated entity types, “organization” is a generic term, whereas, the “corporation” being a sub-type, is more intended towards an enterprise and/or company with some business-related concerns, i.e., every corporation is an organization but not otherwise. Analyzing the corpus-level additive lexical contrast along the type-hierarchy revealed that in addition to sharing the context of the “organization”, the context of the “corporation” is more oriented towards tokens: cents, business, stake, bank, etc. However, for distantly supervised data, it is hard to ensure such a distinctive distribution of contextual tokens for each entity mention. In addition, the lack of information sharing among these distinctive tokens across sentence boundaries leads to poor performance for Phase-I, which makes it a much harder problem from the generalization perspective. A similar scenario holds for other entity types, e.g., actor vs artist vs director, etc. Whereas, after sharing type-specific contextual clues, we can see a drastic improvement in the performance for phase I-II, i.e., F1 = 0.844 compared to F1 = 0.790