Publication Type : Conference Paper
Publisher : IberLEF@ SEPLN
Source : IberLEF@SEPLN (2019)
Url : http://ceur-ws.org/Vol-2421/FACT_paper_1.pdf
Keywords : Factuality classification · Spanish text · Word2vec · Weighted Random Forest
Campus : Amritapuri, Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Computer Science, Electronics and Communication, CISAI
Year : 2019
Abstract : This paper presents the description of the system used by the team Amrita CEN for the shared task on FACT (Factuality Analysis and Classification Task) at IberLEF2019 (Iberian Languages Evaluation Forum) workshop. The goal of the task was to automatically annotate an event with its factuality status. Factuality status is categorized into three as Fact, Counter Fact and Undefined. Our proposed system predicts the factuality of an event with a prediction accuracy of 72.1%. The classification model for this task was trained using Random Forest classifier which uses word embedding of the events as input features. The word embedding of an event was generated by using Word2vec algorithm. Random Forest was implemented by giving higher weights to minority classes and lesser weights to majority classes so that more number of elements in the minority class will be predicted precisely
Cite this Research Publication : B. Premjith, Dr. Soman K. P., and Prabaharan Poornachandran, “Amrita_CEN@FACT: Factuality Identification in Spanish Text”, in IberLEF@SEPLN, 2019.
