Search Results for author: Goran Nenadic

Found 16 papers, 6 papers with code

Examining Large Pre-Trained Language Models for Machine Translation: What You Don't Know About It

no code implementations15 Sep 2022 Lifeng Han, Gleb Erofeev, Irina Sorokina, Serge Gladkoff, Goran Nenadic

Pre-trained language models (PLMs) often take advantage of the monolingual and multilingual dataset that is freely available online to acquire general or mixed domain knowledge before deployment into specific tasks.

Machine Translation

Semantics Altering Modifications for Evaluating Comprehension in Machine Reading

1 code implementation7 Dec 2020 Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro

Advances in NLP have yielded impressive results for the task of machine reading comprehension (MRC), with approaches having been reported to achieve performance comparable to that of humans.

Machine Reading Comprehension

An efficient representation of chronological events in medical texts

no code implementations EMNLP (Louhi) 2020 Andrey Kormilitzin, Nemanja Vaci, Qiang Liu, Hao Ni, Goran Nenadic, Alejo Nevado-Holgado

In this work we addressed the problem of capturing sequential information contained in longitudinal electronic health records (EHRs).

Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models

no code implementations29 May 2020 Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro

Recent years have seen a growing number of publications that analyse Natural Language Inference (NLI) datasets for superficial cues, whether they undermine the complexity of the tasks underlying those datasets and how they impact those models that are optimised and evaluated on this data.

Natural Language Inference

GNTeam at 2018 n2c2: Feature-augmented BiLSTM-CRF for drug-related entity recognition in hospital discharge summaries

no code implementations23 Sep 2019 Maksim Belousov, Nikola Milosevic, Ghada Alfattni, Haifa Alrdahi, Goran Nenadic

The recurrent neural networks that use the pre-trained domain-specific word embeddings and a CRF layer for label optimization perform drug, adverse event and related entities extraction with micro-averaged F1-score of over 91%.

Entity Extraction using GAN named-entity-recognition +2

MedNorm: A Corpus and Embeddings for Cross-terminology Medical Concept Normalisation

1 code implementation WS 2019 Maksim Belousov, William G. Dixon, Goran Nenadic

The medical concept normalisation task aims to map textual descriptions to standard terminologies such as SNOMED-CT or MedDRA.

Representation Learning

Extracting adverse drug reactions and their context using sequence labelling ensembles in TAC2017

no code implementations28 May 2019 Maksim Belousov, Nikola Milosevic, William Dixon, Goran Nenadic

Adverse drug reactions (ADRs) are unwanted or harmful effects experienced after the administration of a certain drug or a combination of drugs, presenting a challenge for drug development and drug administration.

From web crawled text to project descriptions: automatic summarizing of social innovation projects

no code implementations22 May 2019 Nikola Milosevic, Dimitar Marinov, Abdullah Gok, Goran Nenadic

In the past decade, social innovation projects have gained the attention of policy makers, as they address important social issues in an innovative manner.

A framework for information extraction from tables in biomedical literature

1 code implementation26 Feb 2019 Nikola Milosevic, Cassie Gregson, Robert Hernandez, Goran Nenadic

The scientific literature is growing exponentially, and professionals are no more able to cope with the current amount of publications.

Table Detection

Creating a contemporary corpus of similes in Serbian by using natural language processing

no code implementations22 Nov 2018 Nikola Milosevic, Goran Nenadic

Simile is a figure of speech that compares two things through the use of connection words, but where comparison is not intended to be taken literally.

As Cool as a Cucumber: Towards a Corpus of Contemporary Similes in Serbian

1 code implementation20 May 2016 Nikola Milosevic, Goran Nenadic

Similes are natural language expressions used to compare unlikely things, where the comparison is not taken literally.

ManTIME: Temporal expression identification and normalization in the TempEval-3 challenge

no code implementations SEMEVAL 2013 Michele Filannino, Gavin Brown, Goran Nenadic

This paper describes a temporal expression identification and normalization system, ManTIME, developed for the TempEval-3 challenge.

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