Browsing by Author "Sharmila Devi, V."

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    KCE DALab-APDA@FIRE2019: Author profiling and deception detection in Arabic using weighted embedding
    (CEUR-WS ceurws@sunsite.informatik.rwth-aachen.de, 2019) Sharmila Devi, V.; Subramanian, S.; Ravikumar, G.; Anand Kumar, M.A.
    This paper explaining the work submitted on Author Pro- filing and Deception Detection in Arabic Tweets shared task organized at the Forum for Information Retrieval Evaluation (FIRE) 2019. The first task Author profiling illustrates identifying the categories of au- thors based on the Arabic tweets. In the second task, the aim is to Detect deception in Arabic for two genres such as Twitter and News. Deception detection means that the automatic way of identifying false messages in the text content on social network or news. For each task, we have submitted three different systems. For submission 1, we have used the Term Frequency and Inverse Document Frequency (TFIDF) based Support Vector Machine classification and in submission 2, we have used fastText classifier. For submission 3, we have proposed a low dimensional weighted document embedding (TFIDF + Word embedding) with SVM classification. We have attained second place in the Deception detection and third in Author profiling. The performance difference between the top team results and the submitted runs are only 3.34% for Author pro- filing and 1.16% for Deception detection. © Copyright 2019 for this paper by its authors.
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    KCe_Dalab@maponsms-Fire2018: Effective word and character-based features for multilingual author profiling
    (CEUR-WS ceurws@sunsite.informatik.rwth-aachen.de, 2018) Sharmila Devi, V.; Subramanian, S.; Ravikumar, G.; Anand Kumar, M.
    This paper illustrates the work on identification of gender and age-group in Multilingual Author Profiling on SMS messages (MAPonSMS) shared task conducted in the Forum for Information Retrieval and Evaluation (FIRE 2018). To develop the Multilingual Author profiling system, the organizers released the training corpus which includes multilingual (Roman Urdu and English) SMS messages and its corresponding profiles. In gender identification, a profile may be either male or female. The author's age-group fall into one of the three categories: 15-19, 20-24, 25-xx. We have developed the author profiling system 1 using the word and character-based Term Frequency & Inverse Document Frequency (TFIDF) features and classify with Support Vector Machine classifier. The proposed system achieved the State-of-Art performance in the multilingual author profiling on SMS task. The accuracy obtained for identification of age-group is 65% and for gender, it is 87%. The performance is also evaluated jointly where the accuracy gained is 57%. We also experimented with the system by changing different parameters and report the cross-validation accuracy. © 2018 CEUR-WS. All Rights Reserved.