home edit page issue tracker

This page pertains to UD version 2.

UD Telugu English TECT

Language: Telugu English (code: qte)
Family: Code switching

This treebank has been part of Universal Dependencies since the UD v2.14 release.

The following people have contributed to making this treebank part of UD: Anishka Vissamsetty.

Repository: UD_Telugu_English-TECT
Search this treebank on-line: PML-TQ
Download all treebanks: UD 2.14

License: CC BY-SA 4.0

Genre: spoken

Questions, comments? General annotation questions (either Telugu English-specific or cross-linguistic) can be raised in the main UD issue tracker. You can report bugs in this treebank in the treebank-specific issue tracker on Github. If you want to collaborate, please contact [anishka18v (æt) gmail • com]. Development of the treebank happens directly in the UD repository, so you may submit bug fixes as pull requests against the dev branch.

Annotation Source
Lemmas assigned by a program, not checked manually
UPOS annotated manually in non-UD style, automatically converted to UD, with some manual corrections of the conversion
XPOS not available
Features not available
Relations assigned by a program, not checked manually


UD Telugu_English-TECT is a Telugu-English code-switching treebank.

The treebank consists of edited data from the Telugu UD treebank (Rama and Vajilla, 2021), sentences from a grammar book, and the MASSIVE dataset, spoken conversational utterances in Telugu (FitzGerald et al., 2022; Bastianelli et al., 2020). The sentences were randomly selected from each corpus. The sentences were romanized and each sentence was altered to contain at least one code-switch. The sentences were then annotated following the Universal Dependencies annotation scheme.


We want to thank the creators of the Telugu UD treebank and MASSIVE dataset for their corpus.


year = {2021},
title = {The Telugu UD treebank},
author = {Rama, Taraka, Vajjala, Sowmya},
url= {https://github.com/UniversalDependencies/UD_Telugu-MTG}
title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages},
author={Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan},
title = "{SLURP}: A Spoken Language Understanding Resource Package",
author = "Bastianelli, Emanuele and
Vanzo, Andrea and
Swietojanski, Pawel and
Rieser, Verena",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.588",
doi = "10.18653/v1/2020.emnlp-main.588",
pages = "7252--7262",
abstract = "Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are limited. In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement. SLURP is available at https://github.com/pswietojanski/slurp."

Statistics of UD Telugu English TECT

POS Tags






Tokenization and Word Segmentation



Nominal Features

Degree and Polarity

Verbal Features

Pronouns, Determiners, Quantifiers

Other Features


Auxiliary Verbs and Copula

Core Arguments, Oblique Arguments and Adjuncts

Here we consider only relations between verbs (parent) and nouns or pronouns (child).

Relations Overview