scholarly journals Multistage BiCross encoder for multilingual access to COVID-19 health information

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256874
Author(s):  
Iknoor Singh ◽  
Carolina Scarton ◽  
Kalina Bontcheva
Keyword(s):  
Health Information ◽  
State Of The Art ◽  
Information Access ◽  
Semantic Search ◽  
Retrieval Algorithm ◽  
Shared Task ◽  
High Recall ◽  
The Given ◽  
Multiple Languages ◽  
Search And Retrieval

The Coronavirus (COVID-19) pandemic has led to a rapidly growing ‘infodemic’ of health information online. This has motivated the need for accurate semantic search and retrieval of reliable COVID-19 information across millions of documents, in multiple languages. To address this challenge, this paper proposes a novel high precision and high recall neural Multistage BiCross encoder approach. It is a sequential three-stage ranking pipeline which uses the Okapi BM25 retrieval algorithm and transformer-based bi-encoder and cross-encoder to effectively rank the documents with respect to the given query. We present experimental results from our participation in the Multilingual Information Access (MLIA) shared task on COVID-19 multilingual semantic search. The independently evaluated MLIA results validate our approach and demonstrate that it outperforms other state-of-the-art approaches according to nearly all evaluation metrics in cases of both monolingual and bilingual runs.

scholarly journals A novel optimal multi-pattern matching method with wildcards for DNA sequence

2021 ◽  
Vol 29 ◽  
pp. 115-124
Author(s):  
Xinlu Wang ◽  
Ahmed A.F. Saif ◽  
Dayou Liu ◽  
Yungang Zhu ◽  
Jon Atli Benediktsson
Keyword(s):  
Dna Sequence ◽  
Pattern Matching ◽  
Health Informatics ◽  
State Of The Art ◽  
Machine Language ◽  
Data Sets ◽  
Fundamental Issue ◽  
Matching Method ◽  
Dna Sequence Alignment ◽  
The Given

BACKGROUND: DNA sequence alignment is one of the most fundamental and important operation to identify which gene family may contain this sequence, pattern matching for DNA sequence has been a fundamental issue in biomedical engineering, biotechnology and health informatics. OBJECTIVE: To solve this problem, this study proposes an optimal multi pattern matching with wildcards for DNA sequence. METHODS: This proposed method packs the patterns and a sliding window of texts, and the window slides along the given packed text, matching against stored packed patterns. RESULTS: Three data sets are used to test the performance of the proposed algorithm, and the algorithm was seen to be more efficient than the competitors because its operation is close to machine language. CONCLUSIONS: Theoretical analysis and experimental results both demonstrate that the proposed method outperforms the state-of-the-art methods and is especially effective for the DNA sequence.

Report on the 4th Joint Workshop on Bibliometric-Enhanced Information Retrieval and Natural Language Processing for Digital Libraries at SIGIR 2019

2019 ◽  
Vol 53 (2) ◽  
pp. 3-10
Author(s):  
Muthu Kumar Chandrasekaran ◽  
Philipp Mayr
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Research And Development ◽  
Language Processing ◽  
Digital Libraries ◽  
State Of The Art ◽  
Shared Task ◽  
Processing Information ◽  
Joint Workshop

The 4 th joint BIRNDL workshop was held at the 42nd ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019) in Paris, France. BIRNDL 2019 intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The workshop incorporated different paper sessions and the 5 th edition of the CL-SciSumm Shared Task.

scholarly journals Question-aware memory network for multi-hop question answering in human–robot interaction

2021 ◽  
Author(s):  
Xinmeng Li ◽  
Mamoun Alazab ◽  
Qian Li ◽  
Keping Yu ◽  
Quanjun Yin
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Human Robot Interaction ◽  
Knowledge Graph ◽  
Robot Interaction ◽  
Natural Language Question ◽  
Memory Network ◽  
The Given ◽  
Fine Tune ◽  
Language Question

AbstractKnowledge graph question answering is an important technology in intelligent human–robot interaction, which aims at automatically giving answer to human natural language question with the given knowledge graph. For the multi-relation question with higher variety and complexity, the tokens of the question have different priority for the triples selection in the reasoning steps. Most existing models take the question as a whole and ignore the priority information in it. To solve this problem, we propose question-aware memory network for multi-hop question answering, named QA2MN, to update the attention on question timely in the reasoning process. In addition, we incorporate graph context information into knowledge graph embedding model to increase the ability to represent entities and relations. We use it to initialize the QA2MN model and fine-tune it in the training process. We evaluate QA2MN on PathQuestion and WorldCup2014, two representative datasets for complex multi-hop question answering. The result demonstrates that QA2MN achieves state-of-the-art Hits@1 accuracy on the two datasets, which validates the effectiveness of our model.

scholarly journals Health Information Accessed on the Internet: The Development in 5 European Countries

2012 ◽  
Vol 2012 ◽  
pp. 1-3 ◽  
Author(s):  
Per Egil Kummervold ◽  
Rolf Wynn
Keyword(s):  
Health Information ◽  
Major Part ◽  
Information Access ◽  
European Countries ◽  
Northern Europe ◽  
The Internet ◽  
Continuous Growth ◽  
Media Source ◽  
Use Of The Internet ◽  
Near Future

The aim of this study was to summarize and analyse findings from four prior studies on the use of the Internet as a source of health information in five European countries (Norway, Denmark, Germany, Greece, and Portugal). A cross-study comparison of data was performed. All the studies included fit with a trend of a sharp and continuous growth in the use of the Internet for health information access in the major part of the last decade. Importantly, the Internet has become an important mass media source of health information in northern Europe. While the use of the Internet for health information is somewhat less common in the south European countries, its use is also clearly increasing there. We discuss the advantages of cross-study comparisons of data and methodological challenges. As the use of the Internet for health information is likely to peak in some countries in the near future, new population surveys on health information access should focus more on the details of information that is accessed and which sites that are most used and trusted.

Health information needs, sources, and barriers of primary care patients to achieve patient-centered care: A literature review

2016 ◽  
Vol 22 (4) ◽  
pp. 992-1016 ◽  
Author(s):  
Martina A Clarke ◽  
Joi L Moore ◽  
Linsey M Steege ◽  
Richelle J Koopman ◽  
Jeffery L Belden ◽  
...  
Keyword(s):  
Primary Care ◽  
Health Information ◽  
Information Search ◽  
Information Needs ◽  
Information Sources ◽  
Medical Condition ◽  
Information Access ◽  
The Internet ◽  
Common Information ◽  
Primary Care Patients

To synthesize findings from previous studies assessing information needs of primary care patients on the Internet and other information sources in a primary care setting. A systematic review of studies was conducted with a comprehensive search in multiple databases including OVID MEDLINE, CINAHL, and Scopus. The most common information needs among patients were information about an illness or medical condition and treatment methods, while the most common information sources were the Internet and patients’ physicians. Overall, patients tend to prefer the Internet for the ease of access to information, while they trust their physicians more for their clinical expertise and experience. Barriers to information access via the Internet include the following: socio-demographic variables such as age, ethnicity, income, education, and occupation; information search skills; and reliability of health information. Conclusion: Further research is warranted to assess how to create accurate and reliable health information sources for both Internet and non-Internet users.

Semantic Information Retrieval on Medical Texts

2022 ◽  
Vol 54 (7) ◽  
pp. 1-38
Author(s):  
Lynda Tamine ◽  
Lorraine Goeuriot
Keyword(s):  
Information Retrieval ◽  
Health Informatics ◽  
Medical Information ◽  
State Of The Art ◽  
Lessons Learned ◽  
Semantic Search ◽  
Future Research ◽  
Cross Model ◽  
Wide Range ◽  
Search Systems

The explosive growth and widespread accessibility of medical information on the Internet have led to a surge of research activity in a wide range of scientific communities including health informatics and information retrieval (IR). One of the common concerns of this research, across these disciplines, is how to design either clinical decision support systems or medical search engines capable of providing adequate support for both novices (e.g., patients and their next-of-kin) and experts (e.g., physicians, clinicians) tackling complex tasks (e.g., search for diagnosis, search for a treatment). However, despite the significant multi-disciplinary research advances, current medical search systems exhibit low levels of performance. This survey provides an overview of the state of the art in the disciplines of IR and health informatics, and bridging these disciplines shows how semantic search techniques can facilitate medical IR. First,we will give a broad picture of semantic search and medical IR and then highlight the major scientific challenges. Second, focusing on the semantic gap challenge, we will discuss representative state-of-the-art work related to feature-based as well as semantic-based representation and matching models that support medical search systems. In addition to seminal works, we will present recent works that rely on research advancements in deep learning. Third, we make a thorough cross-model analysis and provide some findings and lessons learned. Finally, we discuss some open issues and possible promising directions for future research trends.

scholarly journals Robust Multilingual Named Entity Recognition with Shallow Semi-supervised Features (Extended Abstract)

2017 ◽  
Author(s):  
Rodrigo Agerri ◽  
German Rigau
Keyword(s):  
Reproducibility Of Results ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Local Information ◽  
Entity Recognition ◽  
Shared Task ◽  
Competitive System ◽  
Named Entity ◽  
Text Understanding ◽  
Domain Models

We present a multilingual Named Entity Recognition approach based on a robust and general set of features across languages and datasets. Our system combines shallow local information with clustering semi-supervised features induced on large amounts of unlabeled text. Understanding via empiricalexperimentation how to effectively combine various types of clustering features allows us to seamlessly export our system to other datasets and languages. The result is a simple but highly competitive system which obtains state of the art results across five languages and twelve datasets. The results are reported on standard shared task evaluation data such as CoNLL for English, Spanish and Dutch. Furthermore, and despite the lack of linguistically motivated features, we also report best results for languages such as Basque and German. In addition, we demonstrate that our method also obtains very competitive results even when the amount of supervised data is cut by half, alleviating the dependency on manually annotated data. Finally, the results show that our emphasis on clustering features is crucial to develop robust out-of-domain models. The system and models are freely available to facilitate its use and guarantee the reproducibility of results.

scholarly journals Exemplar Guided Neural Dialogue Generation

2020 ◽  
Author(s):  
Hengyi Cai ◽  
Hongshen Chen ◽  
Yonghao Song ◽  
Xiaofang Zhao ◽  
Dawei Yin
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Training Data ◽  
Small Subset ◽  
Generation Model ◽  
Retrieval Model ◽  
Training Set ◽  
Dialogue Model ◽  
Quantitative Metrics ◽  
The Given

Humans benefit from previous experiences when taking actions. Similarly, related examples from the training data also provide exemplary information for neural dialogue models when responding to a given input message. However, effectively fusing such exemplary information into dialogue generation is non-trivial: useful exemplars are required to be not only literally-similar, but also topic-related with the given context. Noisy exemplars impair the neural dialogue models understanding the conversation topics and even corrupt the response generation. To address the issues, we propose an exemplar guided neural dialogue generation model where exemplar responses are retrieved in terms of both the text similarity and the topic proximity through a two-stage exemplar retrieval model. In the first stage, a small subset of conversations is retrieved from a training set given a dialogue context. These candidate exemplars are then finely ranked regarding the topical proximity to choose the best-matched exemplar response. To further induce the neural dialogue generation model consulting the exemplar response and the conversation topics more faithfully, we introduce a multi-source sampling mechanism to provide the dialogue model with both local exemplary semantics and global topical guidance during decoding. Empirical evaluations on a large-scale conversation dataset show that the proposed approach significantly outperforms the state-of-the-art in terms of both the quantitative metrics and human evaluations.

An IoT-Based Platform for Rehabilitation Monitoring and Biosignal Identification

2018 ◽  
Vol 6 (1) ◽  
pp. 1-19
Author(s):  
Volkhard Klinger
Keyword(s):  
Embedded System ◽  
Medical Technology ◽  
State Of The Art ◽  
Technological Advances ◽  
Measurement Signal ◽  
The Embedded System ◽  
Prosthesis Control ◽  
New Applications ◽  
The Given ◽  
The Internet Of Things

This article describes how as a result of technological advances of the embedded system, the Internet-of-Things (IoT) has created a wealth of new applications and tailored solutions, even in the area of health and medical technology. The integration of state-of-the-art IoT-systems in an existing prototype platform for biosignal acquisition, identification, and prosthesis control provides new applications for prevention and rehabilitation monitoring. This article concentrates on an IoT-based platform for rehabilitation monitoring and biosignal identification. The IoT-characteristics for the application in the area of medical technology are discussed and the integration of such IoT-modules in the given architecture is introduced. Based on this extended architecture, new applications in the field of biosignal measurement, signal processing and biosignal monitoring are presented. Some results of a rehabilitation monitoring system, based on a self-designed IoT-module, integrated in the whole platform, are shown.

scholarly journals Augmented Intention Model for Next-Location Prediction from Graphical Trajectory Context

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Canghong Jin ◽  
Zhiwei Lin ◽  
Minghui Wu
Keyword(s):  
State Of The Art ◽  
Real Life ◽  
Traffic Planning ◽  
Trajectory Prediction ◽  
User Intention ◽  
Convolutional Networks ◽  
Proposed Model ◽  
Gated Recurrent Units ◽  
The Given ◽  
Travel Recommendation

Human trajectory prediction is an essential task for various applications such as travel recommendation, location-sensitive advertisement, and traffic planning. Most existing approaches are sequential-model based and produce a prediction by mining behavior patterns. However, the effectiveness of pattern-based methods is not as good as expected in real-life conditions, such as data sparse or data missing. Moreover, due to the technical limitations of sensors or the traffic situation at the given time, people going to the same place may produce different trajectories. Even for people traveling along the same route, the observed transit records are not exactly the same. Therefore trajectories are always diverse, and extracting user intention from trajectories is difficult. In this paper, we propose an augmented-intention recurrent neural network (AI-RNN) model to predict locations in diverse trajectories. We first propose three strategies to generate graph structures to demonstrate travel context and then leverage graph convolutional networks to augment user travel intentions under graph view. Finally, we use gated recurrent units with augmented node vectors to predict human trajectories. We experiment with two representative real-life datasets and evaluate the performance of the proposed model by comparing its results with those of other state-of-the-art models. The results demonstrate that the AI-RNN model outperforms other methods in terms of top-k accuracy, especially in scenarios with low similarity.

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