1 code implementation • sdp (COLING) 2022 • Mathias Parisot, Jakub Zavrel
Existing dense retrieval models for scientific documents have been optimized for either retrieval by short queries, or for document similarity, but usually not for both.
1 code implementation • 10 Jul 2023 • Hugo Abonizio, Luiz Bonifacio, Vitor Jeronymo, Roberto Lotufo, Jakub Zavrel, Rodrigo Nogueira
Our toolkit not only reproduces the InPars method and partially reproduces Promptagator, but also provides a plug-and-play functionality allowing the use of different LLMs, exploring filtering methods and finetuning various reranker models on the generated data.
1 code implementation • 4 Jan 2023 • Vitor Jeronymo, Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee, Roberto Lotufo, Jakub Zavrel, Rodrigo Nogueira
Recently, InPars introduced a method to efficiently use large language models (LLMs) in information retrieval tasks: via few-shot examples, an LLM is induced to generate relevant queries for documents.
no code implementations • EMNLP (sdp) 2020 • Marzieh Fadaee, Olga Gureenkova, Fernando Rejon Barrera, Carsten Schnober, Wouter Weerkamp, Jakub Zavrel
We give an overview of the overall architecture of the system and of the components for document analysis, question answering, search, analytics, expert search, and recommendations.
1 code implementation • EMNLP (sdp) 2020 • Mark Berger, Jakub Zavrel, Paul Groth
In particular, we explore the impact of the use of contextualized embeddings on search performance.
no code implementations • 22 Dec 1998 • Walter Daelemans, Antal Van den Bosch, Jakub Zavrel
We provide explanations for both results in terms of the properties of the natural language processing tasks and the learning algorithms.
1 code implementation • 11 Jul 1996 • Walter Daelemans, Jakub Zavrel, Peter Berck, Steven Gillis
In this paper we show that a large-scale application of the memory-based approach is feasible: we obtain a tagging accuracy that is on a par with that of known statistical approaches, and with attractive space and time complexity properties when using {\em IGTree}, a tree-based formalism for indexing and searching huge case bases.}