7 research outputs found
ir_metadata: An Extensible Metadata Schema for IR Experiments
The information retrieval (IR) community has a strong tradition of making the
computational artifacts and resources available for future reuse, allowing the
validation of experimental results. Besides the actual test collections, the
underlying run files are often hosted in data archives as part of conferences
like TREC, CLEF, or NTCIR. Unfortunately, the run data itself does not provide
much information about the underlying experiment. For instance, the single run
file is not of much use without the context of the shared task's website or the
run data archive. In other domains, like the social sciences, it is good
practice to annotate research data with metadata. In this work, we introduce
ir_metadata - an extensible metadata schema for TREC run files based on the
PRIMAD model. We propose to align the metadata annotations to PRIMAD, which
considers components of computational experiments that can affect
reproducibility. Furthermore, we outline important components and information
that should be reported in the metadata and give evidence from the literature.
To demonstrate the usefulness of these metadata annotations, we implement new
features in repro_eval that support the outlined metadata schema for the use
case of reproducibility studies. Additionally, we curate a dataset with run
files derived from experiments with different instantiations of PRIMAD
components and annotate these with the corresponding metadata. In the
experiments, we cover reproducibility experiments that are identified by the
metadata and classified by PRIMAD. With this work, we enable IR researchers to
annotate TREC run files and improve the reuse value of experimental artifacts
even further.Comment: Resource pape
Technologies for extracting and analysing the credibility of health-related online content
The evolution of the Web has led to an improvement in
information accessibility. This change has allowed access to
more varied content at greater speed, but we must also be
aware of the dangers involved. The results offered may be
unreliable, inadequate, or of poor quality, leading to
misinformation. This can have a greater or lesser impact
depending on the domain, but is particularly sensitive when it
comes to health-related content.
In this thesis, we focus in the development of methods to
automatically assess credibility. We also studied the reliability of
the new Large Language Models (LLMs) to answer health
questions. Finally, we also present a set of tools that might help
in the massive analysis of web textual content
Pretrained Transformers for Text Ranking: BERT and Beyond
The goal of text ranking is to generate an ordered list of texts retrieved
from a corpus in response to a query. Although the most common formulation of
text ranking is search, instances of the task can also be found in many natural
language processing applications. This survey provides an overview of text
ranking with neural network architectures known as transformers, of which BERT
is the best-known example. The combination of transformers and self-supervised
pretraining has been responsible for a paradigm shift in natural language
processing (NLP), information retrieval (IR), and beyond. In this survey, we
provide a synthesis of existing work as a single point of entry for
practitioners who wish to gain a better understanding of how to apply
transformers to text ranking problems and researchers who wish to pursue work
in this area. We cover a wide range of modern techniques, grouped into two
high-level categories: transformer models that perform reranking in multi-stage
architectures and dense retrieval techniques that perform ranking directly.
There are two themes that pervade our survey: techniques for handling long
documents, beyond typical sentence-by-sentence processing in NLP, and
techniques for addressing the tradeoff between effectiveness (i.e., result
quality) and efficiency (e.g., query latency, model and index size). Although
transformer architectures and pretraining techniques are recent innovations,
many aspects of how they are applied to text ranking are relatively well
understood and represent mature techniques. However, there remain many open
research questions, and thus in addition to laying out the foundations of
pretrained transformers for text ranking, this survey also attempts to
prognosticate where the field is heading
TrialMatch: A Transformer Architecture to Match Patients to Clinical Trials
Around 80% of clinical trials fail to meet the patient recruitment requirements, which
not only hinders the market growth but also delays patients’ access to new and effec-
tive treatments. A possible approach is to use Electronic Health Records (EHRs) to help
match patients to clinical trials. Past attempts at achieving this exact goal took place,
but due to a lack of data, they were unsuccessful. In 2021 Text REtrieval Conference
(TREC) introduced the Clinical Trials Track, where participants were challenged with
retrieving relevant clinical trials given the patient’s descriptions simulating admission
notes. Utilizing the track results as a baseline, we tackled the challenge, for this, we re-
sort to Information Retrieval (IR), implementing a pipeline for document ranking where
we explore the different retrieval methods, how to filter the clinical trials based on the
criteria, and reranking with Transformer based models. To tackle the problem, we ex-
plored models pre-trained on the biomedical domain, how to deal with long queries and
documents through query expansion and passage selection, and how to distinguish an
eligible clinical trial from an excluded clinical trial, using techniques such as Named
Entity Recognition (NER) and Clinical Assertion. Our results let to the finding that the
current state-of-the-art Bidirectional Encoder Representations from Transformers (BERT)
bi-encoders outperform the cross-encoders in the mentioned task, whilst proving that
sparse retrieval methods are capable of obtaining competitive outcomes, and to finalize
we showed that the use of the demographic information available can be used to improve
the final result.Cerca de 80% dos ensaios clínicos não satisfazem os requisitos de recrutamento de paci-
entes, o que não só dificulta o crescimento do mercado como também impede o acesso
dos pacientes a novos e eficazes tratamentos. Uma abordagem possível é utilizar os Pron-
tuários Eletrônicos para ajudar a combinar doentes a ensaios clínicos. Tentativas passadas
para alcançar este exato objetivo tiveram lugar, mas devido à falta de dados, não foram
bem sucedidos. Em 2021, a TREC introduziu a Clinical Trials Track, onde os participantes
foram desafiados com a recuperação ensaios clínicos relevantes, dadas as descrições dos
pacientes simulando notas de admissão. Utilizando os resultados da track como base, en-
frentámos o desafio, para isso recorremos à Recuperação de Informação, implementando
uma pipeline para a classificação de documentos onde exploramos os diferentes métodos
de recuperação, como filtrar os ensaios clínicos com base nos critérios, e reclassificação
com modelos baseados no Transformer. Para enfrentar o problema, explorámos modelos
pré-treinados no domínio biomédico, como lidar com longas descrições e documentos,
e como distinguir um ensaio clínico elegível de um ensaio clínico excluído, utilizando
técnicas como Reconhecimento de Entidade Mencionada e Asserção Clínica. Os nossos re-
sultados permitem concluir que os actuais bi-encoders de última geração BERT superam
os cross-encoders BERT na tarefa mencionada, provamos que os métodos de recuperação
esparsos são capazes de obter resultados competitivos, e para finalizar mostramos que
a utilização da informação demográfica disponível pode ser utilizada para melhorar o
resultado fina
Neural Methods for Effective, Efficient, and Exposure-Aware Information Retrieval
Neural networks with deep architectures have demonstrated significant
performance improvements in computer vision, speech recognition, and natural
language processing. The challenges in information retrieval (IR), however, are
different from these other application areas. A common form of IR involves
ranking of documents--or short passages--in response to keyword-based queries.
Effective IR systems must deal with query-document vocabulary mismatch problem,
by modeling relationships between different query and document terms and how
they indicate relevance. Models should also consider lexical matches when the
query contains rare terms--such as a person's name or a product model
number--not seen during training, and to avoid retrieving semantically related
but irrelevant results. In many real-life IR tasks, the retrieval involves
extremely large collections--such as the document index of a commercial Web
search engine--containing billions of documents. Efficient IR methods should
take advantage of specialized IR data structures, such as inverted index, to
efficiently retrieve from large collections. Given an information need, the IR
system also mediates how much exposure an information artifact receives by
deciding whether it should be displayed, and where it should be positioned,
among other results. Exposure-aware IR systems may optimize for additional
objectives, besides relevance, such as parity of exposure for retrieved items
and content publishers. In this thesis, we present novel neural architectures
and methods motivated by the specific needs and challenges of IR tasks.Comment: PhD thesis, Univ College London (2020
Geographic information extraction from texts
A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction