@inproceedings{bbf9ff74cac348c19d6a0060066fe706,
title = "Cic-IPN@INLi2018: Indian native language identification",
abstract = "{\textcopyright} 2018 CEUR-WS. All Rights Reserved.In this paper, we describe the CIC-IPN submissions to the shared task on Indian Native Language Identification (INLI 2018). We use the Support Vector Machines algorithm trained on numerous feature types: word, character, part-of-speech tag, and punctuation mark n-grams, as well as character n-grams from misspelled words and emotion-based features. The features are weighted using log-entropy scheme. Our team achieved 41.8% accuracy on the test set 1 and 34.5% accuracy on the test set 2, ranking 3rd in the official INLI shared task scoring.",
author = "I. Markov and G. Sidorov",
year = "2018",
language = "English",
volume = "2266",
series = "CEUR Workshop Proceedings",
publisher = "CEUR-WS",
pages = "82--88",
editor = "P. Rosso and P. Mehta and P. Majumder and M. Mitra",
booktitle = "FIRE-WN 2018 - Working Notes of FIRE 2018 - Forum for Information Retrieval Evaluation",
note = "10th Working Notes of FIRE - Forum for Information Retrieval Evaluation, FIRE-WN 2018 ; Conference date: 06-12-2018 Through 09-12-2018",
}