Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes a novel approach to extract opinion targets based on word-based translation model (WTM). At first,  apply WTM in a monolingual scenario to mine the associations beten opinion targets and opinion words. Then, a graph-based algorithm is exploited to extract opinion targets, where candidate opinion relevance estimated from the mined associations, is incorporated with candidate importance to generate a global measure. By using WTM, our method can capture opinion relations more precisely, especially for long-span relations. In particular, compared with previous syntax-based methods, our method can effectively avoid noises from parsing errors when dealing with informal texts in large b corpora. By using graph-based algorithm, opinion targets are extracted in a global process, which can effectively alleviate the problem of error propagation in traditional bootstrap-based methods, such as Double Propagation. The experimental results on three real world datasets in different sizes and languages show that our approach is more effective and robust than state-of-art methods.
['Kang Liu', 'Liheng Xu', 'Jun Zhao']
I am writing a research paper on a idea Opinion Target Extraction Using Word-Based Translation Model
[{"from": "human", "value": "I am writing a research paper on a idea Opinion Target Extraction Using Word-Based Translation Model"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes a novel approach to extract opinion targets based on word-based translation model (WTM). At first,  apply WTM in a monolingual scenario to mine the associations beten opinion targets and opinion words. Then, a graph-based algorithm is exploited to extract opinion targets, where candidate opinion relevance estimated from the mined associations, is incorporated with candidate importance to generate a global measure. By using WTM, our method can capture opinion relations more precisely, especially for long-span relations. In particular, compared with previous syntax-based methods, our method can effectively avoid noises from parsing errors when dealing with informal texts in large b corpora. By using graph-based algorithm, opinion targets are extracted in a global process, which can effectively alleviate the problem of error propagation in traditional bootstrap-based methods, such as Double Propagation. The experimental results on three real world datasets in different sizes and languages show that our approach is more effective and robust than state-of-art methods."}]