On Synergies Between Information Retrieval and Digital Libraries

Agosti, Maristella; Fabris, Erika; Silvello, Gianmaria

doi:10.1007/978-3-030-11226-4_1

Cited by 4 publications

(6 citation statements)

References 10 publications

(18 reference statements)

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“…These information are also reported in a subsequent paper[220] by the same authors 8. https://lucene.apache.org/…”

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confidence: 75%

“…The Scientific Literature document set for TREC PM 2017 (PM17) and 2018 (PM18) consists of a set of 26, 759, 399 MEDLINE abstracts, plus two additional sets: (i) 37, 007 abstracts from recent proceedings of the American Society of Clinical Oncology (ASCO), 8 and (ii) 33, 018 abstracts from recent proceedings of the American Association for Cancer Research (AACR). 9 These additional sets were added to increase the set of potentially relevant treatment information.…”

Section: Scientific Literaturementioning

confidence: 99%

“…Since the very first approaches [116,210], neural IR has attracted a lot of attention: dedicated workshops were held at the ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR) in 2016 [49] and 2017 [47], while an in-depth monograph [165] and a special issue in the Information Retrieval Journal (IRJ) [48] were published in 2018. Also, SIGIR papers employing deep learning are increasing at a fast pace -i.e., from two articles published in 2014 to eleven articles published in 2017 [8] to more than fifty articles published in 2020.…”

Section: Knowledge-enhanced Neural Ir Modelsmentioning

confidence: 99%

“…We use Whoosh to index the considered document collections. 8 Whoosh is a fast search engine library in Python that provides easy access to the underlying tokenized documents -similarly to pyndri [225], used in the original paper. We decided to rely on Whoosh instead of pyndri to keep the entire pipeline in Python.…”

Section: Implementation Detailsmentioning

confidence: 99%

“…The lexical model used before and after query expansion is BM25 with default parameters, implemented in Lucene (p.c.). 8 The number of feedback documents considered by the PRF based method is 3, while the number of words/concepts used to expand the query is 5 (p.c.). It is also worth mentioning that the number of words/concepts considered from each feedback document is 100 (p.c.).…”

Section: Implementation Detailsmentioning

confidence: 99%

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Developing unsupervised knowledge-enhanced models to reduce the semantic gap in information retrieval

Marchesin

2021

SIGIR Forum

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In this thesis we tackle the semantic gap, a long-standing problem in Information Retrieval (IR). The semantic gap can be described as the mismatch between users' queries and the way retrieval models answer to such queries. Two main lines of work have emerged over the years to bridge the semantic gap: (i) the use of external knowledge resources to enhance the bag-of-words representations used by lexical models, and (ii) the use of semantic models to perform matching between the latent representations of queries and documents. To deal with this issue, we first perform an in-depth evaluation of lexical and semantic models through different analyses [Marchesin et al., 2019]. The objective of this evaluation is to understand what features lexical and semantic models share, if their signals are complementary, and how they can be combined to effectively address the semantic gap. In particular, the evaluation focuses on (semantic) neural models and their critical aspects. Each analysis brings a different perspective in the understanding of semantic models and their relation with lexical models. The outcomes of this evaluation highlight the differences between lexical and semantic signals, and the need to combine them at the early stages of the IR pipeline to effectively address the semantic gap. Then, we build on the insights of this evaluation to develop lexical and semantic models addressing the semantic gap. Specifically, we develop unsupervised models that integrate knowledge from external resources, and we evaluate them for the medical domain - a domain with a high social value, where the semantic gap is prominent, and the large presence of authoritative knowledge resources allows us to explore effective ways to address it. For lexical models, we investigate how - and to what extent - concepts and relations stored within knowledge resources can be integrated in query representations to improve the effectiveness of lexical models. Thus, we propose and evaluate several knowledge-based query expansion and reduction techniques [Agosti et al., 2018, 2019; Di Nunzio et al., 2019]. These query reformulations are used to increase the probability of retrieving relevant documents by adding to or removing from the original query highly specific terms. The experimental analyses on different test collections for Precision Medicine - a particular use case of Clinical Decision Support (CDS) - show the effectiveness of the proposed query reformulations. In particular, a specific subset of query reformulations allow lexical models to achieve top performing results in all the considered collections. Regarding semantic models, we first analyze the limitations of the knowledge-enhanced neural models presented in the literature. Then, to overcome these limitations, we propose SAFIR [Agosti et al., 2020], an unsupervised knowledge-enhanced neural framework for IR. SAFIR integrates external knowledge in the learning process of neural IR models and it does not require labeled data for training. Thus, the representations learned within this framework are optimized for IR and encode linguistic features that are relevant to address the semantic gap. The evaluation on different test collections for CDS demonstrate the effectiveness of SAFIR when used to perform retrieval over the entire document collection or to retrieve documents for Pseudo Relevance Feedback (PRF) methods - that is, when it is used at the early stages of the IR pipeline. In particular, the quantitative and qualitative analyses highlight the ability of SAFIR to retrieve relevant documents affected by the semantic gap, as well as the effectiveness of combining lexical and semantic models at the early stages of the IR pipeline - where the complementary signals they provide can be used to obtain better answers to semantically hard queries.

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“…These information are also reported in a subsequent paper[220] by the same authors 8. https://lucene.apache.org/…”

mentioning

confidence: 75%

Section: Scientific Literaturementioning

confidence: 99%