text: Previous approaches to training syntax-based sentiment classification models required phrase-level annotated corpora, which are not readily available in many languages other than English. Thus, we propose the use of tree-structured Long Short-Term Memory with an attention mechanism that pays attention to each subtree of the parse tree. Experimental results indicate that our model achieves the state-of-the-art performance in a Japanese sentiment classification task.
  @cite_30: One of the more powerful impressions created by vision is that of a coherent, richly detailed world where everything is present simultaneously. Indeed, this impression is so compelling that we tend to ascribe these properties not only to the external world, but to our internal representations as well. But results from several recent experiments argue against this latter ascription. For example, changes in images of real-world scenes often go unnoticed when made during a saccade, flicker, blink, or movie cut. This “change blindness” provides strong evidence against the idea that our brains contain a picture-like representation of the scene that is everywhere detailed and coherent. How then do we represent a scene? It is argued here that focused attention provides spatiotemporal coherence for the stable representation of one object at a time. It is then argued that the allocation of attention can be co-ordinated to create a “virtual representation”. In such a scheme, a stable object representation is formed...
summary: Based on psychological studies, the human ability of intuition of attention @cite_30 has been introduced into many computer science fields. The main function of this ability is deciding which part of an input needs to be focused on.