Active Learning for Reducing Labeling Effort in Text Classification Tasks

Autor: Jacobs, Pieter Floris, Maillette De Buy Wenniger, Gideon, Wiering, Marco, Schomaker, Lambert, Leiva, L.A., Pruski, C, Markovich, R., Najjar, A., Schommer, C.

Publikováno v: Communications in Computer and Information Science ISBN: 9783030938413
33rd Benelux Conference on Artificial Intelligence, BNAIC/Benelearn 2021: Esch-sur-Alzette, Luxembourg, November 10–12, 2021, Revised Selected Papers, 3-29
STARTPAGE=3;ENDPAGE=29;TITLE=33rd Benelux Conference on Artificial Intelligence, BNAIC/Benelearn 2021

Labeling data can be an expensive task as it is usually performed manually by domain experts. This is cumbersome for deep learning, as it is dependent on large labeled datasets. Active learning (AL) is a paradigm that aims to reduce labeling effort b

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bb6d4c3652a0a142dbb2ad3a3e5136c8
https://doi.org/10.1007/978-3-030-93842-0_1

SChuBERT: Scholarly Document Chunks with BERT-encoding boost Citation Count Prediction

Autor: van Dongen, Thomas, Maillette de Buy Wenniger, Gideon, Schomaker, Lambert, Chandrasekaran, Muthu Kumar

Publikováno v: Proceedings of the First Workshop on Scholarly Document Processing, 148-157
STARTPAGE=148;ENDPAGE=157;TITLE=Proceedings of the First Workshop on Scholarly Document Processing

Predicting the number of citations of scholarly documents is an upcoming task in scholarly document processing. Besides the intrinsic merit of this information, it also has a wider use as an imperfect proxy for quality which has the advantage of bein

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7d08eaaeacf729c1f3653e245e297382
https://research.rug.nl/en/publications/d13d2b5a-db30-418e-b2a9-2e41951ba4ac

Labeling hierarchical phrase-based models without linguistic resources.

Autor: Maillette de Buy Wenniger, Gideon, Sima'an, Khalil

Publikováno v: Machine Translation; Dec2015, Vol. 29 Issue 3/4, p225-265, 41p

Investigating Query Expansion and Coreference Resolution in Question Answering on BERT

Autor: Gideon Maillette de Buy Wenniger, Santanu Bhattacharjee, Andy Way, Rejwanul Haque

Publikováno v: Natural Language Processing and Information Systems
Bhattacharjee, Santanu, Haque, Rejwanul ORCID: 0000-0003-1680-0099 , Maillette de Buy Wenniger, Gideon and Way, Andy ORCID: 0000-0001-5736-5930 (2020) Investigating query expansion and coreference resolution in question answering on BERT. In: 25th International Conference on Natural Language & Information Systems (NLDB 2020)), 24-26 June 2020, Saarbrücken, Germany (Online). ISBN 978-3-030-51309-2
Natural Language Processing and Information Systems ISBN: 9783030513092
NLDB
Natural Language Processing and Information Systems-25th International Conference on Applications of Natural Language to Information Systems, NLDB 2020, Saarbrücken, Germany, June 24–26, 2020, Proceedings
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Natural Language Processing and Information Systems

The Bidirectional Encoder Representations from Transformers (BERT) model produces state-of-the-art results in many question answering (QA) datasets, including the Stanford Question Answering Dataset (SQuAD). This paper presents a query expansion (QE)

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fafa70b774c91a7c920e211a1b177b7d
http://europepmc.org/articles/PMC7298170

Improving transductive data selection algorithms for machine translation

Autor: Poncelas, Alberto

Publikováno v: Poncelas, Alberto ORCID: 0000-0002-5089-1687 (2019) Improving transductive data selection algorithms for machine translation. PhD thesis, Dublin City University.

In this work, we study different ways of improving Machine Translation models by using the subset of training data that is the most relevant to the test set. This is achieved by using Transductive Algoritms (TA) for data selection. In particular, we

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______119::30290dcc644a4074acd06ed70e8bf009
http://doras.dcu.ie/23726/