Abstract | Our experimental results show that the two proposed models are indeed able to perform the task effectively. |
Experiments | This section evaluates the proposed models . |
Experiments | Even with this noisy automatically-labeled data, the proposed models can produce good results. |
Experiments | However, it is important to note that the proposed models are flexible and do not need to have seeds for every aspect/topic. |
Introduction | The proposed models are evaluated using a large number of hotel reviews. |
Introduction | Experimental results show that the proposed models outperform the two baselines by large margins. |
Related Work | We will show in Section 4 that the proposed models outperform it by a large margin. |
Abstract | By fitting parameters to maximize the likelihood of the bilingual parallel data, the proposed model learns previously unseen sentiment words from the large bilingual parallel data and improves vocabulary coverage significantly. |
Conclusion and Future Work | First, the proposed model can learn previously unseen sentiment words from large unlabeled data, which are not covered by the limited vocabulary in machine translation of the labeled data. |
Experiment | Table 2 shows the accuracy of the baseline systems as well as the proposed model (CLMM). |
Introduction | By “synchronizing” the generation of words in the source language and the target language in a parallel corpus, the proposed model can (1) improve vocabulary coverage by learning sentiment words from the unlabeled parallel corpus; (2) transfer polarity label information between the source language and target language using a parallel corpus. |
Introduction | This paper makes two contributions: (1) we propose a model to effectively leverage large bilingual parallel data for improving vocabulary coverage; and (2) the proposed model is applicable in both settings of cross-lingual sentiment classification, irrespective of the availability of labeled data in the target language. |
Model | 4.2 Baseline and Proposed Models |
Model | We use the following baseline and proposed models for evaluation. |
Model | Figure 2 shows the F1 scores of the proposed model (SegTagDep) on CTB-Sc-l with respect to the training epoch and different parsing feature weights, where “Seg”, “Tag”, and “Dep” respectively denote the F1 scores of word segmentation, POS tagging, and dependency parsing. |