izvorni znanstveni rad
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.
Negation ; noisy labels ; labelling functions ; unsupervised learning
Razvojem virtualizacijskih tehnologija raste kompleksnost IT sustava, dok korisnici istovremeno zahtijevaju brži pristup resursima, napredne integracije i jednostavnost korištenja. Jedno od ključnih rješenja za ove izazove primjena je…
Ovaj rad istražuje i uspoređuje mogućnosti prosljeđivanja grafičkih kartica na Hyper-V i KVM hipervizorima prema virtualnim strojevima, detaljno opisujući metode, postupke i razlike u arhitekturi koje utječu na…
The ever-growing usage and adaptation of HTTP/3 in web traffic leverages Quick UDP Internet Connections (QUIC) as a transport protocol. QUIC is widely recognized for reduced latency, enhanced…
