Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present our participation in Task 2 of the 2013 CLEF- eHEALTH Challenge, whose goal was to determine the UMLS concept unique identier (CUI), if available, of an abbreviation or acronym.  hypothesize that considering only the abbreviations of the training cor- pus could be sucient to provide a strong baseline for this task.  therefore test how a fully supervised approach, which predicts the CUI of an abbreviation based only on the abbreviations and CUIs seen in the training corpus, can fare on this task.  adapt to this task the process- ing pipeline  developed for CLEF-eHEALTH Task 1, entity detection: a supervised MaxEnt model based on a set of features including UMLS Concept Unique Identiers, complemented here with a rule-based com- ponent for document headers. This system conrmed our hypothesis, and was evaluated at 0.664 accuracy (strict) and 0.672 accuracy (relaxed), ranking second out of ve teams. The 2013 CLEF-eHEALTH challenge (9) aims to develop methods and resources that make electronic medical records (EMRs) more understandable by both pa- tients and health professionals. The challenge spans three tasks. The second task focuses on the resolution of abbreviations into UMLS concept unique identiers (CUIs).  present here our participation in the second task and develop a sys- tem that can determine the CUI of an abbreviation.  propose a solution that combines a simple feature set with external knowledge gathered from the UMLS Metathesaurus. Clinical notes often use abbreviations and acronyms (henceforth collectively named 'abbreviations' in this paper). While the number and variety of abbre- viations is unlimited thanks to the plasticity of language and the inventiveness of the human mind, the distribution of abbreviations can be expected to fol- low a Zipan law, with a small number of distinct abbreviations accounting for a large part of the total number of occurrences of abbreviations.  therefore
['Pierre Zweigenbaum', 'Louise Deléger', 'Thomas Lavergne', 'Aurélie Névéol', 'Andreea Bodnari']
I am writing a research paper on a idea A Supervised Abbreviation Resolution System for Medical Text
[{"from": "human", "value": "I am writing a research paper on a idea A Supervised Abbreviation Resolution System for Medical Text"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present our participation in Task 2 of the 2013 CLEF- eHEALTH Challenge, whose goal was to determine the UMLS concept unique identier (CUI), if available, of an abbreviation or acronym.  hypothesize that considering only the abbreviations of the training cor- pus could be sucient to provide a strong baseline for this task.  therefore test how a fully supervised approach, which predicts the CUI of an abbreviation based only on the abbreviations and CUIs seen in the training corpus, can fare on this task.  adapt to this task the process- ing pipeline  developed for CLEF-eHEALTH Task 1, entity detection: a supervised MaxEnt model based on a set of features including UMLS Concept Unique Identiers, complemented here with a rule-based com- ponent for document headers. This system conrmed our hypothesis, and was evaluated at 0.664 accuracy (strict) and 0.672 accuracy (relaxed), ranking second out of ve teams. The 2013 CLEF-eHEALTH challenge (9) aims to develop methods and resources that make electronic medical records (EMRs) more understandable by both pa- tients and health professionals. The challenge spans three tasks. The second task focuses on the resolution of abbreviations into UMLS concept unique identiers (CUIs).  present here our participation in the second task and develop a sys- tem that can determine the CUI of an abbreviation.  propose a solution that combines a simple feature set with external knowledge gathered from the UMLS Metathesaurus. Clinical notes often use abbreviations and acronyms (henceforth collectively named 'abbreviations' in this paper). While the number and variety of abbre- viations is unlimited thanks to the plasticity of language and the inventiveness of the human mind, the distribution of abbreviations can be expected to fol- low a Zipan law, with a small number of distinct abbreviations accounting for a large part of the total number of occurrences of abbreviations.  therefore"}]