Naslov
Negation Detection Using NooJ

Autori
Thakkar, Gaurish ; Mikelić Preradović, Nives ; Tadić, Marko

Vrsta, podvrsta i kategorija rada
Poglavlja u knjigama, znanstveni

Knjiga
Proceedings of the 44th International Convention on Information and Communication Technology, Electronics and Microelectronics MIPRO 2021

Urednik/ci
Skala, Karolj

Izdavač
Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO

Grad
Rijeka

Godina
2021

Raspon stranica
263-267

ISSN
1847-3938

Ključne riječi
Negation ; noisy labels ; labelling functions ; unsupervised learning

Sažetak
The availability of extensive annotated data for natural language processing tasks is an unsolved problem. Transfer learning techniques usually mitigate these issues by relying on existing models in another language. If no such models exist, the whole transfer learning setup becomes an implausible option. This paper presents a simple approach to use grammar rule as a noisy labelling function to train a classic generative- discriminative classification setup. The approach relies on a simple NooJ grammar along with a series of other data labelling functions. We evaluate the approach on the Conan- Doyle dataset for the task of explicit negation detection with a lowresource setting and report an improvement of 2% over the baseline.

Izvorni jezik
Engleski

Znanstvena područja
Informacijske i komunikacijske znanosti

POVEZANOST RADA