Traditional mathematical search models retrieve scientific documents only by mathematical expressions and their contexts and do not consider the ontological attributes of scientific documents, which result in gaps between the queries and the retrieva...
The rapid increase in scientific publications has made it challenging to keep up with the latest advancements. Conducting systematic reviews using traditional methods is both time-consuming and difficult. To address this, new review formats like rapi...
Transformer-based models face persistent challenges in long-document summarization due to fixed input-length constraints. Hybrid approaches address this limitation by applying extractive preprocessing to select salient sentences for downstream abstra...
Recently, the performance of machine-reading and comprehension (MRC) systems has been significantly enhanced. However, MRC systems require high-performance text retrieval models because text passages containing answer phrases should be prepared in ad...
Extractive summarization aims to produce a concise version of a document by extracting information-rich sentences from the original texts. The graph-based model is an effective and efficient approach to rank sentences since it is simple and easy to u...
In the area of text summarization, there have been significant advances recently. In the meantime, the current trend in text summarization is focused more on news summarization. Therefore, developing a synthesis approach capable of extracting, compar...
Handwritten Keyword Spotting (KWS) remains a challenging task, particularly in segmentation-free scenarios where word images must be retrieved and ranked based on their similarity to a query without relying on prior page-level segmentation. Tradition...
Applications for document similarity detection are widespread in diverse communities, including institutions and corporations. However, currently available detection systems fail to take into account the private nature of material or documents that h...
The rapid growth of digital journalism has heightened the need for reliable multi-document summarization (MDS) systems, particularly in underrepresented, low-resource, and culturally distinct contexts. However, current progress is hindered by a lack...
With the advent of cloud computing, the low-cost and high-capacity cloud storages have attracted people to move their data from local computers to the remote facilities. People can access and share their data with others at anytime, from anywhere. Ho...
Learning to Rank (L2R) methods that utilize machine learning techniques to solve the ranking problems have been widely studied in the field of information retrieval. Existing methods usually concatenate query and document features as training input,...
Evaluation of document classification is straightforward if complete information on the documents’ true categories exists. In this case, the rank of each document can be accurately determined and evaluated. However, in an unsupervised setting,...
In recent years, entity-based ranking models have led to exciting breakthroughs in the research of information retrieval. Compared with traditional retrieval models, entity-based representation enables a better understanding of queries and documents....
This paper proposes a new methodology to study sequential corpora by implementing a two-stage algorithm that learns time-based topics with respect to a scale of document positions and introduces the concept of Topic Scaling, which ranks learned topic...
In the area of consumer health search (CHS), there is an increasing concern about returning topically relevant and understandable health information to the user. Besides being used to rank topically relevant documents, Learning to Rank (LTR) has also...
In this paper, we construct an academic literature knowledge graph based on the relationship between documents to facilitate the storage and research of academic literature data. Keywords are an important type of node in the knowledge graph. To solve...
Similar patent document retrieval is an essential task that reduces the scope of patent claimants’ searches, and numerous studies have attempted to provide automated patent search services. Recently, Retrieval-Augmented Generation (RAG) based o...
Automatic Keyphrase Extraction involves identifying essential phrases in a document. These keyphrases are crucial in various tasks, such as document classification, clustering, recommendation, indexing, searching, summarization, and text simplificati...
28 November 2014
Climate change puts pressure on existing health vulnerabilities through higher frequency of extreme weather events, changes in disease vector distribution or exacerbated air pollution. Climate change adaptation policies may hold potential to reduce s...
Ad hoc information retrieval (ad hoc IR) is a challenging task consisting of ranking text documents for bag-of-words (BOW) queries. Classic approaches based on query and document text vectors use term-weighting functions to rank the documents. Some o...
The visibility of academic articles or conference papers depends on their being easily found in academic search engines, above all in Google Scholar. To enhance this visibility, search engine optimization (SEO) has been applied in recent years to aca...
Web search engines are built from components capable of processing large amounts of user queries per second in a distributed way. Among them, the index service computes the top-k documents that best match each incoming query by means of a document ra...
The rapid growth of Internet technologies has led to an enormous increase in the number of electronic documents used worldwide. To organize and manage big data for unstructured documents effectively and efficiently, text categorization has been emplo...
Research front detection and topic evolution has for a long time been an important direction for research in the informetrics field. However, most previous studies either simply use a citation count for scientific document clustering or assume that e...
Quantum-inspired algorithms represent an important direction in modern software information technologies that use heuristic methods and approaches of quantum science. This work presents a quantum approach for document search, retrieval, and ranking b...
This study proposes the optimization method of the associative knowledge graph using TF-IDF based ranking scores. The proposed method calculates TF-IDF weights in all documents and generates term ranking. Based on the terms with high scores from TF-I...
With the emergence of the metaverse, the popularity of augmented reality (AR) is increasing; accessing concise, accurate, and precise information in this field is becoming challenging on the world wide web. In regard to accessing the right informatio...
In this paper, we describe our biomedical document retrieval system and answers extraction module, which is part of the biomedical question answering system. Approximately 26.5 million PubMed articles are indexed as a corpus with the Apache Lucene te...
Over the past decade, knowledge bases (KB) have been increasingly utilized to complete and enrich the representation of queries and documents in order to improve the document retrieval task. Although many approaches have used KB for such purposes, th...
Information Retrieval is not any more exclusively about document ranking. Continuously new tasks are proposed on this and sibling fields. With this proliferation of tasks, it becomes crucial to have a cheap way of constructing test collections to eva...
In relation to “objective risk” or “subjective risk”, a bibliometric analysis was performed using documents found in the Scopus database. A search for related documents was narrowed down to 192 documents and these were considered in this study. The r...
To support evidence-based precision medicine and clinical decision-making, we need to identify accurate, appropriate, and clinically relevant studies from voluminous biomedical literature. To address the issue of accurate identification of high impac...
We present the application of Latent Semantic Analysis (LSA) in combination with recommender systems, in order to enhance discovery in geoportals. As a basis for discovery, metadata of spatial data and services, as well as of non-spatial resources, s...
This paper presents a hybrid document recommender system intended for use in digital libraries and institutional repositories that are part of the Slovenian Open Access Infrastructure. The recommender system provides recommendations of similar docume...
Automatic extractive text summarization retrieves a subset of data that represents most notable sentences in the entire document. In the era of digital explosion, which is mostly unstructured textual data, there is a demand for users to understand th...
Keyphrase extraction is a critical task in text information retrieval, which traditionally employs both supervised and unsupervised approaches. Supervised methods generally rely on large corpora, which introduce the problems of availability, while un...
Research trends in the field of forestry have experienced a significant evolution in recent years. However, there has been little use of bibliometric analyses to assess academic organizations and individual researchers in this field of science. This...
In this paper we perform a comparative analysis of three models for a feature representation of text documents in the context of document classification. In particular, we consider the most often used family of bag-of-words models, the recently propo...
In the recent big data era, massive spatial related data are continuously generated and scrambled from various sources. Acquiring accurate geographic information is also urgently demanded. How to accurately retrieve desired geographic information has...
Urban documents like city planning reports and environmental data often feature complex charts and texts that require effective summarization tools, particularly in smart city management systems. These documents increasingly use graphical abstracts a...
Nowadays, pre-trained language models such as Bidirectional Encoder Representations from Transformer (BERT) are becoming a basic building block in Information Retrieval tasks. Nevertheless, there are several limitations when applying BERT to the quer...
Learning-to-rank has been intensively studied and has shown significantly increasing values in a wide range of domains, such as web search, recommender systems, dialogue systems, machine translation, and even computational biology, to name a few. In...
In cloud computing, documents can be outsourced to the cloud server to achieve flexible access control and efficient sharing among multiple users. The outsourced documents can be intelligently searched according to some keywords with the help of clou...
Tools for Natural Language Processing work using linguistic resources, that are language-specific. The complexity of building such resources causes many languages to lack them. So, learning them automatically from sample texts would be a desirable so...
21 September 2024
Children and young people constitute a structurally vulnerable group who often experience specific barriers when trying to exercise their rights, including the right to health. The aim of this study was to examine core concepts of human rights and in...
We address the problem of scalable content-based search in large collections of music documents. Music content is highly complex and versatile and presents multiple facets that can be considered independently or in combination. Moreover, music docume...
This paper aims to comprehensively review 891 documents in the Scopus database about Internet of Things (IoT) in Ind 4.0 to understand the historical growth, current state, and potential expansion trend. From 2014 to 2020, a systematic methodology ga...
Document classification has a broad application in the field of sentiment classification, document ranking and topic labeling, etc. Previous neural network-based work has mainly focused on investigating a so-called forward implication, i.e., the prec...
In this article, we introduce a dataset of curated learning paths (LPs) to support search as learning. LPs were obtained through an online survey delivered to experts in different domains. Data were then analyzed and described in terms of a set of va...
The building industry is one of the most resource-intensive sectors in industrialized countries, requiring a shift from a linear to a more sustainable circular economic model. Nevertheless, there are several major challenges, such as the management o...
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