CaMEL: Case Marker Extraction without Labels

03/18/2022
by   Leonie Weissweiler, et al.
0

We introduce CaMEL (Case Marker Extraction without Labels), a novel and challenging task in computational morphology that is especially relevant for low-resource languages. We propose a first model for CaMEL that uses a massively multilingual corpus to extract case markers in 83 languages based only on a noun phrase chunker and an alignment system. To evaluate CaMEL, we automatically construct a silver standard from UniMorph. The case markers extracted by our model can be used to detect and visualise similarities and differences between the case systems of different languages as well as to annotate fine-grained deep cases in languages in which they are not overtly marked.

READ FULL TEXT

page 1

page 2

page 3

page 4

03/16/2022

Morphological Processing of Low-Resource Languages: Where We Are and What's Next

Automatic morphological processing can aid downstream natural language p...
10/15/2021

Integrating diverse extraction pathways using iterative predictions for Multilingual Open Information Extraction

In this paper we investigate a simple hypothesis for the Open Informatio...
04/26/2018

Lessons from the Bible on Modern Topics: Low-Resource Multilingual Topic Model Evaluation

Multilingual topic models enable document analysis across languages thro...
06/30/2022

Domain Adaptive Pretraining for Multilingual Acronym Extraction

This paper presents our findings from participating in the multilingual ...
02/15/2023

Meeting the Needs of Low-Resource Languages: The Value of Automatic Alignments via Pretrained Models

Large multilingual models have inspired a new class of word alignment me...
05/05/2022

Quantifying Language Variation Acoustically with Few Resources

Deep acoustic models represent linguistic information based on massive a...
08/19/2019

Tale of tails using rule augmented sequence labeling for event extraction

The problem of event extraction is a relatively difficult task for low r...

Please sign up or login with your details

Forgot password? Click here to reset