Question answering for Biology

Neves, Mariana; Leser, Ulf

doi:10.1016/j.ymeth.2014.10.023

Cited by 32 publications

(24 citation statements)

References 42 publications

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“…HPI1: our previous system that participated in the BioASQ challenge last year (Neves, 2015); 2. HPI2: our new QA system, which is described in this work.…”

Section: Systemsmentioning

confidence: 99%

“…Question answering (QA) comes as an alternative to standard search engines system, e.g. PubMed 1 , and provides precise and short answers for questions in natural language (Athenikos and Han, 2010;Neves and Leser, 2015). One of the advantages of QA systems is that the user does not need to be proficient in formulating queries in a way that the system can understand.…”

Section: Introductionmentioning

confidence: 99%

“…We submitted runs from two QA systems which were specifically developed for the biomedical domain. One of the system (HPI1) successfully participated in the previous editions of the BioASQ challenge (Neves, 2015) and our second system (HPI2) is described in this work. We relied on existing NLP functionality from a inmemory database (IMDB) and we extend it with new procedures tailored specifically to QA.…”

Section: Introductionmentioning

confidence: 99%

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Proceedings of the Fourth BioASQ workshop

2016

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ISBN 978-1-945626-22-7 ACL anthology W16-31xxii PrefaceThe fourth BioASQ workshop on biomedical semantic indexing and question answering took place in Berlin, Germany on August 13th, 2016 and was hosted by the Humboldt University. The workshop was supported by the BioASQ project 1 , which organizes the corresponding annual challenge. The goals of the workshop were to present the results of the fourth BioASQ challenge and further the interaction with the wider community of biomedical semantic indexing and question answering. The presenters represented research teams from different parts of the globe and with different viewpoints to the problem. This contributed to a very lively and interesting discussion among the participants of the workshop. Six papers were presented during the workshop. All were selected by peer review for presentation. This volume includes 7 papers and one abstract: The first paper gives an overview of the challenge, including especially the datasets that were used throughout the challenges and the overall results achieved by the participants. The remaining six papers are those presented at the workshop. The first of these papers is a new extension of the MTI system. In particular, Learning to Rank methodology is used as a boosting component of the MTI system. The second paper present a system which includes several multi-label classifiers (MLC) that are combined in ensembles. Elastic-Search for indexing is the object of discourse of the third workshop paper. The fourth paper presents a system which uses TmTool in addition to MetaMap, to identify possible biomedical named entities, especially out-of-vocabulary concepts. They also introduced a unified classification interface for judging the relevance of each retrieved concept, document, and snippet, which can combine the relevant scores evidenced by various sources. The system presented in the fifth paper relies on the Hana Database for text processing. It uses the Stanford CoreNLP package for tokenizing the questions. Each of the tokens is then sent to the BioPortal and to the Hana database for concept retrieval. The concepts retrieved from the two stores are finally merged to a single list that is used to retrieve relevant text passages from the documents at hand. The last paper focuses on the retrieval of relevant documents and snippets. The proposed system uses a cluster-based language model. Then, it reranks the retrieved top-n sentences using five independent similarity models based on shallow semantic analysis.Finally, the proceedings also include the abstract of one paper that was presented in the poster session only, which describes an approach for extending the web services in order to retrieve the relevant documents, concepts, snippets and triples for the question-answering task.We wish to thank all who participated to the success of this workshop, especially the authors, reviewers, speakers and participants. AbstractThe goal of this task is to push the research frontier towards hybrid information systems. We aim to promote syste...

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“…HPI1: our previous system that participated in the BioASQ challenge last year (Neves, 2015); 2. HPI2: our new QA system, which is described in this work.…”

Section: Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Proceedings of the Fourth BioASQ workshop

2016

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“…Therefore, QA systems need to rely on various natural language processing (NLP) components, such as question understanding, namedentity recognition (NER), document and passage retrieval, answer extraction and multi-document summarization, among others. QA systems have been developed for many domains, including biomedicine (Athenikos and Han, 2010;Neves and Leser, 2015). Given the large collection of biomedical documents, e.g., in PubMed, researchers and physicians need to obtain answers for their various questions in a timely manner.…”

Section: Introductionmentioning

confidence: 99%

Assessing the performance of Olelo, a real-time biomedical question answering application

Neves¹,

Eckert²,

Folkerts³

et al. 2017

BioNLP 2017

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Question answering (QA) can support physicians and biomedical researchers to find answers to their questions in the scientific literature. Such systems process large collections of documents in real time and include many natural language processing (NLP) procedures. We recently developed Olelo, a QA system for biomedicine which includes various NLP components, such as question processing, document and passage retrieval, answer processing and multi-document summarization. In this work, we present an evaluation of our system on the the fifth BioASQ challenge. We participated with the current state of the application and with an extension based on semantic role labeling that we are currently investigating. In addition to the BioASQ evaluation, we compared our system to other on-line biomedical QA systems in terms of the response time and the quality of the answers.

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“…Question answering (QA) can support biomedical professionals by allowing input in the form of natural questions and by providing exact answers and customized short summaries in return (Athenikos and Han, 2010;Neves and Leser, 2015). We are aware of three of such systems for biomedicine (cf.…”

Section: Introductionmentioning

confidence: 99%

Olelo: A Question Answering Application for Biomedicine

Neves

Folkerts

Jankrift

et al. 2017

Proceedings of ACL 2017, System Demonstrations

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Despite the importance of the biomedical domain, there are few reliable applications to support researchers and physicians for retrieving particular facts that fit their needs. Users typically rely on search engines that only support keywordand filter-based searches. We present Olelo, a question answering system for biomedicine. Olelo is built on top of an inmemory database, integrates domain resources, such as document collections and terminologies, and uses various natural language processing components. Olelo is fast, intuitive and easy to use. We evaluated the systems on two use cases: answering questions related to a particular gene and on the BioASQ benchmark.Olelo is available at: http://hpi.de/ plattner/olelo.

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Question answering for Biology

Cited by 32 publications

References 42 publications

Proceedings of the Fourth BioASQ workshop

Proceedings of the Fourth BioASQ workshop

Assessing the performance of Olelo, a real-time biomedical question answering application

Olelo: A Question Answering Application for Biomedicine

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