Pith. sign in

REVIEW

Strategies for Language Identification in Code-Mixed Low Resource Languages

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1810.07156 v2 pith:JMRCVMN5 submitted 2018-10-16 cs.CL

Soumil Mandal , Sankalp Sanand This is my paper · ORCID
classification cs.CL
keywords accuracycode-mixeddatalanguagearoundbuildstrategiessystem
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

In recent years, substantial work has been done on language tagging of code-mixed data, but most of them use large amounts of data to build their models. In this article, we present three strategies to build a word level language tagger for code-mixed data using very low resources. Each of them secured an accuracy higher than our baseline model, and the best performing system got an accuracy around 91%. Combining all, the ensemble system achieved an accuracy of around 92.6%.

Discussion (0). Continue with ORCID to comment.

Pith tools