Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: High impact events, political changes and new technologies are reflected in our language and lead to constant evolution of terms, expressions and names. Not knowing about names used in the past for referring to a named entity can severely decrease the performance of many computational linguistic algorithms.  propose NEER, an unsupervised method for named entity evolution recognition independent of external knowledge sources.  find time periods with high likelihood of evolution. By analyzing only these time periods using a sliding window co-occurrence method  capture evolving terms in the same context.  thus avoid comparing terms from widely different periods in time and overcome a severe limitation of existing methods for named entity evolution, as shown by the high recall of 90% on the New York Times corpus.  compare several relatedness measures for filtering to improve precision. Furthermore, using machine learning with minimal supervision improves precision to 94%.
['Nina Tahmasebi', 'Gerhard Gossen', 'Nattiya Kanhabua', 'Helge Holzmann', 'Thomas Risse']
I am writing a research paper on a idea NEER: An Unsupervised Method for Named Entity Evolution Recognition
[{"from": "human", "value": "I am writing a research paper on a idea NEER: An Unsupervised Method for Named Entity Evolution Recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: High impact events, political changes and new technologies are reflected in our language and lead to constant evolution of terms, expressions and names. Not knowing about names used in the past for referring to a named entity can severely decrease the performance of many computational linguistic algorithms.  propose NEER, an unsupervised method for named entity evolution recognition independent of external knowledge sources.  find time periods with high likelihood of evolution. By analyzing only these time periods using a sliding window co-occurrence method  capture evolving terms in the same context.  thus avoid comparing terms from widely different periods in time and overcome a severe limitation of existing methods for named entity evolution, as shown by the high recall of 90% on the New York Times corpus.  compare several relatedness measures for filtering to improve precision. Furthermore, using machine learning with minimal supervision improves precision to 94%."}]