1 code implementation • EACL (Louhi) 2021 • Anne Dirkson, Suzan Verberne, Wessel Kraaij
Using FuzzyBIO also improves end-to-end performance for continuous and composite entities in two of three data sets.
no code implementations • ACL (NLP4PosImpact) 2021 • Myrthe Reuver, Nicolas Mattis, Marijn Sax, Suzan Verberne, Nava Tintarev, Natali Helberger, Judith Moeller, Sanne Vrijenhoek, Antske Fokkens, Wouter van Atteveldt
In this position paper, we present a research agenda and ideas for facilitating exposure to diverse viewpoints in news recommendation.
no code implementations • EACL (Hackashop) 2021 • Myrthe Reuver, Antske Fokkens, Suzan Verberne
Natural Language Processing (NLP) is defined by specific, separate tasks, with each their own literature, benchmark datasets, and definitions.
no code implementations • SMM4H (COLING) 2020 • Anne Dirkson, Suzan Verberne, Wessel Kraaij
We experiment with two approaches to add conversational context to a BERT model: a sequential CRF layer and manually engineered features.
no code implementations • EMNLP (ArgMining) 2021 • Myrthe Reuver, Suzan Verberne, Roser Morante, Antske Fokkens
Our attention then turns to the cross-topic aspect of this work, and the specificity of topics in terms of vocabulary and socio-cultural context.
no code implementations • 20 May 2024 • Fons Hartendorp, Tom Seinen, Erik van Mulligen, Suzan Verberne
We use MedRoBERTa. nl as base model and perform second-phase pretraining through self-alignment on a Dutch biomedical ontology extracted from the UMLS and Dutch SNOMED.
2 code implementations • 5 Apr 2024 • Myrthe Reuver, Suzan Verberne, Antske Fokkens
In this paper, we investigate the robustness of operationalization choices for few-shot stance detection, with special attention to modelling stance across different topics.
no code implementations • 27 Mar 2024 • Gineke Wiggers, Suzan Verberne, Arjen de Vries, Roel van der Burg
We show these challenges with log data from a live legal search system and two user studies.
no code implementations • 27 Mar 2024 • Amin Abolghasemi, Zhaochun Ren, Arian Askari, Mohammad Aliannejadi, Maarten de Rijke, Suzan Verberne
In this work, we leverage large language models (LLMs) and unlock their ability to generate satisfaction-aware counterfactual dialogues to augment the set of original dialogues of a test collection.
no code implementations • 9 Mar 2024 • Amin Abolghasemi, Leif Azzopardi, Arian Askari, Maarten de Rijke, Suzan Verberne
With TExFAIR, we extend the AWRF framework to allow for the evaluation of fairness in settings with term-based representations of groups in documents in a ranked list.
no code implementations • 5 Mar 2024 • Zhengliang Shi, Shen Gao, Xiuyi Chen, Lingyong Yan, Haibo Shi, Dawei Yin, Zhumin Chen, Pengjie Ren, Suzan Verberne, Zhaochun Ren
Tool learning empowers large language models (LLMs) as agents to use external tools to extend their capability.
1 code implementation • 18 Feb 2024 • Arian Askari, Roxana Petcu, Chuan Meng, Mohammad Aliannejadi, Amin Abolghasemi, Evangelos Kanoulas, Suzan Verberne
Furthermore, we propose SOLID-RL, which is further trained to generate a dialog in one step on the data generated by SOLID.
no code implementations • 2 Feb 2024 • Jiabao Fang, Shen Gao, Pengjie Ren, Xiuying Chen, Suzan Verberne, Zhaochun Ren
First, we design a multi-agent act planning framework, which can control the dialogue flow based on four LLM-based agents.
1 code implementation • 9 Jan 2024 • Arian Askari, Zihui Yang, Zhaochun Ren, Suzan Verberne
Furthermore, we propose LegalQA: a real-world benchmark dataset for evaluating answer retrieval in the legal domain.
1 code implementation • 21 Dec 2023 • Rayden Tseng, Suzan Verberne, Peter van der Putten
ChatGPT, GPT-3. 5, and other large language models (LLMs) have drawn significant attention since their release, and the abilities of these models have been investigated for a wide variety of tasks.
no code implementations • 31 Oct 2023 • Bram M. A. van Dijk, Max J. van Duijn, Suzan Verberne, Marco R. Spruit
ChiSCor was compiled for studying how children render character perspectives, and unravelling language and cognition in development, with computational tools.
no code implementations • 12 Sep 2023 • Sophia Althammer, Guido Zuccon, Sebastian Hofstätter, Suzan Verberne, Allan Hanbury
We further find that gains provided by AL strategies come at the expense of more assessments (thus higher annotation costs) and AL strategies underperform random selection when comparing effectiveness given a fixed annotation cost.
no code implementations • 4 Sep 2023 • Joost Broekens, Bernhard Hilpert, Suzan Verberne, Kim Baraka, Patrick Gebhard, Aske Plaat
Large language models, in particular generative pre-trained transformers (GPTs), show impressive results on a wide variety of language-related tasks.
1 code implementation • 3 May 2023 • Arian Askari, Mohammad Aliannejadi, Evangelos Kanoulas, Suzan Verberne
We introduce a new dataset, ChatGPT-RetrievalQA, and compare the effectiveness of models fine-tuned on LLM-generated and human-generated data.
1 code implementation • 17 Apr 2023 • Hugo de Vos, Suzan Verberne
We release the resulting corpus and our analysis pipeline for future research.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
1 code implementation • 2 Mar 2023 • Arian Askari, Suzan Verberne, Amin Abolghasemi, Wessel Kraaij, Gabriella Pasi
Furthermore, our method solves the problem of the low-resource training in QBD retrieval tasks as it does not need large amounts of training data, and has only three parameters with a limited range that can be optimized with a grid search even if a small amount of labeled data is available.
1 code implementation • 23 Jan 2023 • Arian Askari, Amin Abolghasemi, Gabriella Pasi, Wessel Kraaij, Suzan Verberne
In this paper we propose a novel approach for combining first-stage lexical retrieval models and Transformer-based re-rankers: we inject the relevance score of the lexical model as a token in the middle of the input of the cross-encoder re-ranker.
1 code implementation • 11 Oct 2022 • Amin Abolghasemi, Arian Askari, Suzan Verberne
In this work, we examine the generalizability of two of these deep contextualized term-based models in the context of query-by-example (QBE) retrieval in which a seed document acts as the query to find relevant documents.
1 code implementation • 14 Aug 2022 • Sophia Althammer, Sebastian Hofstätter, Suzan Verberne, Allan Hanbury
Robust test collections are crucial for Information Retrieval research.
no code implementations • 26 May 2022 • Arian Askari, Georgios Peikos, Gabriella Pasi, Suzan Verberne
Our methodology consists of four steps; in detail, given a legal case as a query, we reformulate it by extracting various meaningful sentences or n-grams.
1 code implementation • 5 Apr 2022 • Yanfang Hou, Peter van der Putten, Suzan Verberne
During the COVID-19 pandemic, large amounts of COVID-19 misinformation are spreading on social media.
no code implementations • 1 Feb 2022 • Amin Abolghasemi, Suzan Verberne, Leif Azzopardi
Query-by-document (QBD) retrieval is an Information Retrieval task in which a seed document acts as the query and the goal is to retrieve related documents -- it is particular common in professional search tasks.
1 code implementation • 19 Jan 2022 • Arian Askari, Suzan Verberne, Gabriella Pasi
In the legal domain, there is a large knowledge gap between the experts and the searchers, and the content on the legal QA websites consist of a combination formal and informal communication.
1 code implementation • 5 Jan 2022 • Sophia Althammer, Sebastian Hofstätter, Mete Sertkan, Suzan Verberne, Allan Hanbury
However in the web domain we are in a setting with large amounts of training data and a query-to-passage or a query-to-document retrieval task.
no code implementations • 14 Oct 2021 • Myrthe Reuver, Suzan Verberne, Roser Morante, Antske Fokkens
Cross-topic stance detection is the task to automatically detect stances (pro, against, or neutral) on unseen topics.
no code implementations • 27 Sep 2021 • Hugo de Vos, Suzan Verberne
In an often-cited 2019 paper on the use of machine learning in political research, Anastasopoulos & Whitford (A&W) propose a text classification method for tweets related to organizational reputation.
1 code implementation • 23 Sep 2021 • Anne Dirkson, Suzan Verberne, Wessel Kraaij
Experimental results show that BERT models are vulnerable to variation in the entity context with 20. 2 to 45. 0% of entities predicted completely wrong and another 29. 3 to 53. 3% of entities predicted wrong partially.
1 code implementation • 9 Aug 2021 • Sophia Althammer, Arian Askari, Suzan Verberne, Allan Hanbury
We address this challenge by combining lexical and dense retrieval methods on the paragraph-level of the cases for the first stage retrieval.
no code implementations • 14 Jun 2021 • Alex Brandsen, Suzan Verberne, Karsten Lambers, Milco Wansleeben
We also investigate ensemble methods for combining multiple BERT models, and combining the best BERT model with a domain thesaurus using Conditional Random Fields (CRF).
1 code implementation • 9 Jun 2021 • Ioannis Chios, Suzan Verberne
This indicates that the explainability of the search engine result page leads to a better user experience.
no code implementations • 7 Mar 2021 • Alex Brandsen, Suzan Verberne, Karsten Lambers, Milco Wansleeben
We conducted a user study for the evaluation of AGNES's search interface, with a small but diverse user group.
1 code implementation • 4 Jan 2021 • Ken Voskuil, Suzan Verberne
In this paper we build on prior work using Conditional Random Fields (CRF) and Flair for reference extraction.
1 code implementation • COLING 2020 • Yuting Hu, Suzan Verberne
There is a large body of work on Biomedical Entity Recognition (Bio-NER) for English but there have only been a few attempts addressing NER for Chinese biomedical texts.
no code implementations • 19 Oct 2020 • Jeroen Offerijns, Suzan Verberne, Tessa Verhoef
In this work, we train a GPT-2 language model to generate three distractors for a given question and text context, using the RACE dataset.
no code implementations • LREC 2020 • Hugo de Vos, Suzan Verberne
Challenges of Applying Automatic Speech Recognition for Transcribing EUParliament Committee Meetings: A Pilot StudyHugo de Vos and Suzan VerberneInstitute of Public Administration and Leiden Institute of Advanced Computer Science, Leiden Universityh. p. de. vos@fgga. leidenuniv. nl, s. verberne@liacs. leidenuniv. nlAbstractWe tested the feasibility of automatically transcribing committee meetings of the European Union parliament with the use of AutomaticSpeech Recognition techniques.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • LREC 2020 • Br, Alex sen, Suzan Verberne, Milco Wansleeben, Karsten Lambers
In this paper, we present the development of a training dataset for Dutch Named Entity Recognition (NER) in the archaeology domain.
2 code implementations • 2 Oct 2019 • Benjamin van der Burgh, Suzan Verberne
We evaluated the effectiveness of using language models, that were pre-trained in one domain, as the basis for a classification model in another domain: Dutch book reviews.
1 code implementation • WS 2019 • Anne Dirkson, Suzan Verberne
Transfer learning is promising for many NLP applications, especially in tasks with limited labeled data.
no code implementations • WS 2019 • Anne Dirkson, Suzan Verberne, Wessel Kraaij
In the medical domain, user-generated social media text is increasingly used as a valuable complementary knowledge source to scientific medical literature.
no code implementations • 11 May 2019 • Suzan Verberne, Jiyin He, Gineke Wiggers, Tony Russell-Rose, Udo Kruschwitz, Arjen P. de Vries
Search conducted in a work context is an everyday activity that has been around since long before the Web was invented, yet we still seem to understand little about its general characteristics.