Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Measuring the similarity beten semantic relations that hold among entities is an important and necessary step in various b related tasks such as relation extraction, information retrieval and analogy detection. For example, consider the case in which a person knows a pair of entities (e.g. Google, YouTube), beten which a particular relation holds (e.g. acquisition). The person is interested in retrieving other such pairs with similar relations (e.g. Microsoft, Porset). Existing keyword-based search engines cannot be applied directly in this case because, in keyword-based search, the goal is to retrieve documents that are relevant to the words used in a query -- not necessarily to the relations implied by a pair of words.  propose a relational similarity measure, using a b search engine, to compute the similarity beten semantic relations implied by two pairs of words. Our method has three components: representing the various semantic relations that exist beten a pair of words using automatically extracted lexical patterns, clustering the extracted lexical patterns to identify the different patterns that express a particular semantic relation, and measuring the similarity beten semantic relations using a metric learning approach.  evaluate the proposed method in two tasks: classifying semantic relations beten named entities, and solving word-analogy questions. The proposed method outperforms all baselines in a relation classification task with a statistically significant average precision score of 0.74. Moreover, it reduces the time taken by Latent Relational Analysis to process 374 word-analogy questions from 9 days to less than 6 hours, with an SAT score of 51%.
['Danushka Bollegala', 'Yutaka Matsuo', 'Mitsuru Ishizuka']
I am writing a research paper on a idea Measuring the similarity between implicit semantic relations from the web
[{"from": "human", "value": "I am writing a research paper on a idea Measuring the similarity between implicit semantic relations from the web"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Measuring the similarity beten semantic relations that hold among entities is an important and necessary step in various b related tasks such as relation extraction, information retrieval and analogy detection. For example, consider the case in which a person knows a pair of entities (e.g. Google, YouTube), beten which a particular relation holds (e.g. acquisition). The person is interested in retrieving other such pairs with similar relations (e.g. Microsoft, Porset). Existing keyword-based search engines cannot be applied directly in this case because, in keyword-based search, the goal is to retrieve documents that are relevant to the words used in a query -- not necessarily to the relations implied by a pair of words.  propose a relational similarity measure, using a b search engine, to compute the similarity beten semantic relations implied by two pairs of words. Our method has three components: representing the various semantic relations that exist beten a pair of words using automatically extracted lexical patterns, clustering the extracted lexical patterns to identify the different patterns that express a particular semantic relation, and measuring the similarity beten semantic relations using a metric learning approach.  evaluate the proposed method in two tasks: classifying semantic relations beten named entities, and solving word-analogy questions. The proposed method outperforms all baselines in a relation classification task with a statistically significant average precision score of 0.74. Moreover, it reduces the time taken by Latent Relational Analysis to process 374 word-analogy questions from 9 days to less than 6 hours, with an SAT score of 51%."}]