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==Papers== #[http://malt.ml.cmu.edu/mw/index.php/Birke%26Sarkar,FigLanguages07 Active learning for the identification of nonliteral language (Birke07)] #[http://malt.ml.cmu.edu/mw/index.php/A_Clustering_Approach_for_the_Nearly_Unsupervised_Recognition_of_Nonliteral_Language,_EACL-2006 A Clustering Approach for the Nearly Unsupervised Recognition of Nonliteral Language (Birke06)] == Big Idea == * The (Birke 07) paper is the further development of (Birke 06). In (Birke 06), they use the '''literal feedback seed''' and '''non-literal feedback seed set''' to do the word sense disambiguation of the verb in a given sentence, and thus distinguish the non-literal uses from literal uses. However, the result in (Birke 06) is apparently noisy that they adopted some heuristic rules and a voting mechanism to clean the data. In (Birke 07), they use '''active learning''' instead of heuristic rules or voting, so that the human annotation can more effectively improve the result but not only "clean" them. == Methodology == Both the papers have build a [[UsesMethod::Logistic regression]] model using an extensive list of features. <p>Anderson et al have proposed the solutions of two problems - Predicting the long term value of a question and Predicting whether a question has been sufficiently answered. There are features which are common to both the problems, as well as features specific to the problem.</p> <p> Liu et al have divided the task into three subtasks - query clarity, query-answer match and answer satisfaction. It has defined features pertaining to each task. Further, they have defined two forms of Logisitc Regression. - Direct and Combined. In Direct Logistic Regression they combine all the features of all the three subtasks and come up with one model. In Composite Logistic Regression the model for each subtask is trained separately and then combined to come up with one model. </p> == Dataset == Anderson et al have used [[UsesDataset::Stack Overflow|Stack Overflow Data ]] for the study. Liu et al have used [[UsesDataset::Click_Dataset_Google_Yahoo|Click Dataset on Google search leading to Yahoo! Answers]]. == Evaluation == The evaluation metric of both the papers are different. Where Anderson et al measures the accuracy and Area under the ROC curve ( AUC), Liu et al uses Correlation and RMSE as the evaluation metric. Anderson et al have stated that their metric is close to the ground truth, Liu et al states that they their methodology gives solves a novel problem with a high correlation with the anwers of the human judges. == Other Questions == * How much time did you spend reading the (new, non-wikified) paper you summarized? ** 4 hours. * How much time did you spend reading the old wikified paper? ** 30 mins. * How much time did you spend reading the summary of the old paper? ** 1.5 hour. * How much time did you spend reading background material? ** The problem is very relevant to my own research project, so not much, only about 1 hour. * Was there a study plan for the old paper? ** Yes *** if so, did you read any of the items suggested by the study plan? and how much time did you spend with reading them? **** Not really the same paper, but I took a look at some other word sense disambiguation papers. Totally maybe about 1.5 hour. * Give us any additional feedback you might have about this assignment. ** This is a good assignment let us really understand what those papers about.

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