Investigating Machine Learning Methods for Language and Dialect Identification of Cuneiform Texts

by   Ehsan Doostmohammadi, et al.

Identification of the languages written using cuneiform symbols is a difficult task due to the lack of resources and the problem of tokenization. The Cuneiform Language Identification task in VarDial 2019 addresses the problem of identifying seven languages and dialects written in cuneiform; Sumerian and six dialects of Akkadian language: Old Babylonian, Middle Babylonian Peripheral, Standard Babylonian, Neo-Babylonian, Late Babylonian, and Neo-Assyrian. This paper describes the approaches taken by SharifCL team to this problem in VarDial 2019. The best result belongs to an ensemble of Support Vector Machines and a naive Bayes classifier, both working on character-level features, with macro-averaged F1-score of 72.10


Experiments in Cuneiform Language Identification

This paper presents methods to discriminate between languages and dialec...

Abusive and Threatening Language Detection in Urdu using Supervised Machine Learning and Feature Combinations

This paper presents the system descriptions submitted at the FIRE Shared...

Comparing Approaches to Dravidian Language Identification

This paper describes the submissions by team HWR to the Dravidian Langua...

The WiLI benchmark dataset for written language identification

This paper describes the WiLI-2018 benchmark dataset for monolingual wri...

Absit invidia verbo: Comparing Deep Learning methods for offensive language

This document describes our approach to building an Offensive Language C...

Labeling of Query Words using Conditional Random Field

This paper describes our approach on Query Word Labeling as an attempt i...