Transformation Networks for Target-Oriented Sentiment Classification The work was done when Xin Li was an intern at Tencent AI Lab. This project is substantially supported by a grant from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Code: 14203414).
Table 3: Experimental results (%). The results with symbol“♮” are retrieved from the original papers, and those starred (∗) one are from Dong et al. (2014). The marker † refers to p-value < 0.01 when comparing with BILSTM-ATT-G, while the marker ‡ refers to p-value < 0.01 when comparing with RAM.
['[EMPTY]', '[BOLD] Models', 'LAPTOP', 'LAPTOP', 'REST', 'REST', 'TWITTER', 'TWITTER']
Moreover, TNet can perform well for different kinds of user generated content, such as product reviews with relatively formal sentences in LAPTOP and REST, and tweets with more ungrammatical sentences in TWITTER. The reason is the CNN-based feature extractor arms TNet with more power to extract accurate features from ungrammatical sentences. Indeed, we can also observe that another CNN-based baseline, i.e., CNN-ASP implemented by us, also obtains good results on TWITTER. It shows that the integration of target information into the word-level representations is crucial for good performance. (position relevance is kept for them).