Bidirectional Recursive Neural Networksfor Token-Level Labeling with Structure
Table 1: An example sentence with labels
['The B_HOLDER', 'Committee I_HOLDER', ', O', 'as B_ESE', 'usual I_ESE', ', O', 'and O', 'as O']
This is important for tasks that require a fine-grained opinion analysis, such as opinion oriented question answering and opinion summarization. DSEs consist of explicit mentions of private states or speech events expressing private states, and ESEs consist of expressions that indicate sentiment, emotion, etc., without explicitly conveying them. indirectly expresses the attitude of the writer. On the task of ESE detection, the Combined network has significantly better binary F-measure compared to others (58.71 compared to 58.03 and 56.73). Furthermore, the Combined network has significantly better proportional precision than the two other architectures, at an insignificant loss in proportional recall. In terms of binary measures, the Bi-Recursive network has low precision and high recall, which might suggest a complementary behavior for the two architectures. ESEs tend to be longer relative to DSEs, which might explain the results. Aditionally, unlike DSEs, ESEs more often require contextual information for their interpretation.