Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A machine-readable bilingual dictionary plays a crucial role in many natural language processing tasks, such as statistical machine translation and cross-language information retrieval. In this article,  propose a framework for extracting a bilingual dictionary from comparable corpora by exploiting a novel combination of topic modeling and word aligners such as the IBM models. Using a multilingual topic model,  first convert a comparable  document -aligned corpus into a parallel  topic -aligned corpus. This novel topic-aligned corpus is similar in structure to the  sentence -aligned corpus frequently employed in statistical machine translation and allows us to extract a bilingual dictionary using a word alignment model.   The main advantages of our framework is that (1) no seed dictionary is necessary for bootstrapping the process, and (2) multilingual comparable corpora in more than two languages can also be exploited. In our experiments on a large-scale Wikipedia dataset,  demonstrate that our approach can extract higher precision dictionaries compared to previous approaches and that our method improves further as  add more languages to the dataset.
['Xiaodong Liu', 'Kevin Duh', 'Yuji Matsumoto']
I am writing a research paper on a idea Multilingual Topic Models for Bilingual Dictionary Extraction
[{"from": "human", "value": "I am writing a research paper on a idea Multilingual Topic Models for Bilingual Dictionary Extraction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A machine-readable bilingual dictionary plays a crucial role in many natural language processing tasks, such as statistical machine translation and cross-language information retrieval. In this article,  propose a framework for extracting a bilingual dictionary from comparable corpora by exploiting a novel combination of topic modeling and word aligners such as the IBM models. Using a multilingual topic model,  first convert a comparable  document -aligned corpus into a parallel  topic -aligned corpus. This novel topic-aligned corpus is similar in structure to the  sentence -aligned corpus frequently employed in statistical machine translation and allows us to extract a bilingual dictionary using a word alignment model.   The main advantages of our framework is that (1) no seed dictionary is necessary for bootstrapping the process, and (2) multilingual comparable corpora in more than two languages can also be exploited. In our experiments on a large-scale Wikipedia dataset,  demonstrate that our approach can extract higher precision dictionaries compared to previous approaches and that our method improves further as  add more languages to the dataset."}]