332 research outputs found

    VoMBaT: A Tool for Visualising Evaluation Measure Behaviour in High-Recall Search Tasks

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    The objective of High-Recall Information Retrieval (HRIR) is to retrieve as many relevant documents as possible for a given search topic. One approach to HRIR is Technology-Assisted Review (TAR), which uses information retrieval and machine learning techniques to aid the review of large document collections. TAR systems are commonly used in legal eDiscovery and systematic literature reviews. Successful TAR systems are able to find the majority of relevant documents using the least number of assessments. Commonly used retrospective evaluation assumes that the system achieves a specific, fixed recall level first, and then measures the precision or work saved (e.g., precision at r% recall). This approach can cause problems related to understanding the behaviour of evaluation measures in a fixed recall setting. It is also problematic when estimating time and money savings during technology-assisted reviews. This paper presents a new visual analytics tool to explore the dynamics of evaluation measures depending on recall level. We implemented 18 evaluation measures based on the confusion matrix terms, both from general IR tasks and specific to TAR. The tool allows for a comparison of the behaviour of these measures in a fixed recall evaluation setting. It can also simulate savings in time and money and a count of manual vs automatic assessments for different datasets depending on the model quality. The tool is open-source, and the demo is available under the following URL: https://vombat.streamlit.app

    Overview of the TREC 2022 NeuCLIR Track

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    This is the first year of the TREC Neural CLIR (NeuCLIR) track, which aims to study the impact of neural approaches to cross-language information retrieval. The main task in this year's track was ad hoc ranked retrieval of Chinese, Persian, or Russian newswire documents using queries expressed in English. Topics were developed using standard TREC processes, except that topics developed by an annotator for one language were assessed by a different annotator when evaluating that topic on a different language. There were 172 total runs submitted by twelve teams.Comment: 22 pages, 13 figures, 10 tables. Part of the Thirty-First Text REtrieval Conference (TREC 2022) Proceedings. Replace the misplaced Russian result tabl

    NeuralMind-UNICAMP at 2022 TREC NeuCLIR: Large Boring Rerankers for Cross-lingual Retrieval

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    This paper reports on a study of cross-lingual information retrieval (CLIR) using the mT5-XXL reranker on the NeuCLIR track of TREC 2022. Perhaps the biggest contribution of this study is the finding that despite the mT5 model being fine-tuned only on query-document pairs of the same language it proved to be viable for CLIR tasks, where query-document pairs are in different languages, even in the presence of suboptimal first-stage retrieval performance. The results of the study show outstanding performance across all tasks and languages, leading to a high number of winning positions. Finally, this study provides valuable insights into the use of mT5 in CLIR tasks and highlights its potential as a viable solution. For reproduction refer to https://github.com/unicamp-dl/NeuCLIR22-mT

    Geographic information extraction from texts

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    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

    KenSwQuAD -- A Question Answering Dataset for Swahili Low Resource Language

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    The need for Question Answering datasets in low resource languages is the motivation of this research, leading to the development of Kencorpus Swahili Question Answering Dataset, KenSwQuAD. This dataset is annotated from raw story texts of Swahili low resource language, which is a predominantly spoken in Eastern African and in other parts of the world. Question Answering (QA) datasets are important for machine comprehension of natural language for tasks such as internet search and dialog systems. Machine learning systems need training data such as the gold standard Question Answering set developed in this research. The research engaged annotators to formulate QA pairs from Swahili texts collected by the Kencorpus project, a Kenyan languages corpus. The project annotated 1,445 texts from the total 2,585 texts with at least 5 QA pairs each, resulting into a final dataset of 7,526 QA pairs. A quality assurance set of 12.5% of the annotated texts confirmed that the QA pairs were all correctly annotated. A proof of concept on applying the set to the QA task confirmed that the dataset can be usable for such tasks. KenSwQuAD has also contributed to resourcing of the Swahili language.Comment: 17 pages, 1 figure, 10 table

    Facilitating Information Access for Heterogeneous Data Across Many Languages

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    Information access, which enables people to identify, retrieve, and use information freely and effectively, has attracted interest from academia and industry. Systems for document retrieval and question answering have helped people access information in powerful and useful ways. Recently, natural language technologies based on neural network have been applied to various tasks for information access. Specifically, transformer-based pre-trained models have pushed tasks such as document and passage retrieval to new state-of-the-art effectiveness. (1) Most of the research has focused on helping people access passages and documents on the web. However, there is abundant information stored in other formats such as semi-structured tables and domain-specific relational databases in companies. Development of the models and frameworks that support access information from these data formats is also essential. (2) Moreover, most of the advances in information access research are based on English, leaving other languages less explored. It is insufficient and inequitable in our globalized and connected world to serve only speakers of English. In this thesis, we explore and develop models and frameworks that could alleviate the aforementioned challenges. This dissertation consists of three parts. We begin with a discussion on developing models designed for accessing data in formats other than passages and documents. We mainly focus on two data formats, namely semi-structured tables and relational databases. In the second part, we discuss methods that can enhance the user experience for non-English speakers when using information access systems. Specifically, we first introduce model development for multilingual knowledge graph integration, which can benefit many information access applications such as cross-lingual question answering systems and other knowledge-driven cross-lingual NLP applications. We further focus on multilingual document dense retrieval and reranking that boost the effectiveness of search engines for non-English information access. Last but not least, we take a step further based on the aforementioned two parts by investigating models and frameworks that can facilitate non-English speakers to access structured data. In detail, we present cross-lingual Text-to-SQL semantic parsing systems that enable non-English speakers to query relational databases with queries in their languages

    Current state and innovations in newborn screening: continuing to do good and avoid harm

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    In 1963, Robert Guthrie’s pioneering work developing a bacterial inhibition assay to measure phenylalanine in dried blood spots, provided the means for whole-population screening to detect phenylketonuria in the USA. In the following decades, NBS became firmly established as a part of public health in developed countries. Technological advances allowed for the addition of new disorders into routine programmes and thereby resulted in a paradigm shift. Today, technological advances in immunological methods, tandem mass spectrometry, PCR techniques, DNA sequencing for mutational variant analysis, ultra-high performance liquid chromatography (UPLC), iso-electric focusing, and digital microfluidics are employed in the NBS laboratory to detect more than 60 disorders. In this review, we will provide the current state of methodological advances that have been introduced into NBS. Particularly, ‘second-tier’ methods have significantly improved both the specificity and sensitivity of testing. We will also present how proteomic and metabolomic techniques can potentially improve screening strategies to reduce the number of false-positive results and improve the prediction of pathogenicity. Additionally, we discuss the application of complex, multiparameter statistical procedures that use large datasets and statistical algorithms to improve the predictive outcomes of tests. Future developments, utilizing genomic techniques, are also likely to play an increasingly important role, possibly combined with artificial intelligence (AI)-driven software. We will consider the balance required to harness the potential of these new advances whilst maintaining the benefits and reducing the risks for harm associated with all screening

    A Unified Framework for Learned Sparse Retrieval

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    Learned sparse retrieval (LSR) is a family of first-stage retrieval methods that are trained to generate sparse lexical representations of queries and documents for use with an inverted index. Many LSR methods have been recently introduced, with Splade models achieving state-of-the-art performance on MSMarco. Despite similarities in their model architectures, many LSR methods show substantial differences in effectiveness and efficiency. Differences in the experimental setups and configurations used make it difficult to compare the methods and derive insights. In this work, we analyze existing LSR methods and identify key components to establish an LSR framework that unifies all LSR methods under the same perspective. We then reproduce all prominent methods using a common codebase and re-train them in the same environment, which allows us to quantify how components of the framework affect effectiveness and efficiency. We find that (1) including document term weighting is most important for a method's effectiveness, (2) including query weighting has a small positive impact, and (3) document expansion and query expansion have a cancellation effect. As a result, we show how removing query expansion from a state-of-the-art model can reduce latency significantly while maintaining effectiveness on MSMarco and TripClick benchmarks. Our code is publicly available at https://github.com/thongnt99/learned-sparse-retrieva
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