KSAnswer: Question-answering System of Kangwon National University
            and Sogang University in the 2016 BioASQ Challenge

Lee, Hyeon-gu; Kim, Minkyoung; Kim, Harksoo; Kim, Juae; Kwon, Sunjae; Seo, Jungyun; Kim, Yi-Reun; Choi, Jungkyu

doi:10.18653/v1/w16-3106

Cited by 5 publications

(4 citation statements)

References 28 publications

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“…The 1000 most relevant documents are retrieved from the previous step. These documents are split into trigram units [15]. A trigram unit is generated by the sliding window technique.…”

Section: Document Retrievalmentioning

confidence: 99%

“…Candidate snippets are retrieved by using a cluster-based language model. Five independent similarity models are used to re-rank the retrieved top-N snippets [15]. The "AUTH" team has tried approaches based on BioASQ search services and ElasticSearch for the document retrieval task by querying the top 10 documents, with a combination of words in each question [16].…”

Section: Introductionmentioning

confidence: 99%

“…That is why we propose suggestions to improve our score in the coming section. Reference MAP [6] 0.0903 [12] 0.1099 [11] 0.2264 Proposed System 0.2150 [5] 0.3083 [15] 0.5333…”

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confidence: 99%

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Document Retrieval System for Biomedical Question Answering

Bolat,

Şen

2024

Applied Sciences

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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 text search engine. Our proposed system consists of three parts. The first part is the question analysis module, which analyzes the question and enriches it with biomedical concepts related to its wording. The second part of the system is the document retrieval module. In this step, the proposed system is tested using different information retrieval models, like the Vector Space Model, Okapi BM25, and Query Likelihood. The third part is the document re-ranking module, which is responsible for re-arranging the documents retrieved in the previous step. For this study, we tested our proposed system with 6B training questions from the BioASQ challenge task. We obtained the best MAP score on the document retrieval phase when we used Query Likelihood with the Dirichlet Smoothing model. We used the sequential dependence model at the re-rank phase, but this model produced a worse MAP score than the previous phase. In similarity calculation, we included the Named Entity Recognition (NER), UMLS Concept Unique Identifiers (CUI), and UMLS Semantic Types of the words in the question to find the sentences containing the answer. Using this approach, we observed a performance enhancement of roughly 25% for the top 20 outcomes, surpassing another method employed in this study, which relies solely on textual similarity.

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“…The 1000 most relevant documents are retrieved from the previous step. These documents are split into trigram units [15]. A trigram unit is generated by the sliding window technique.…”

Section: Document Retrievalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Document Retrieval System for Biomedical Question Answering

Bolat,

Şen

2024

Applied Sciences

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“…Lee et al [11] have introduced KSAnswer biomedical QA system in BioASQ 2016. KSAnswer was tested in the BioASQ task 4b phase A challenge.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Pre-training for Biomedical Question Answering

Kommaraju,

Gunasekaran,

et al. 2020

Preprint

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We explore the suitability of unsupervised representation learning methods on biomedical text -BioBERT, SciBERT, and Bio-SentVec -for biomedical question answering. To further improve unsupervised representations for biomedical QA, we introduce a new pretraining task from unlabeled data designed to reason about biomedical entities in the context. Our pre-training method consists of corrupting a given context by randomly replacing some mention of a biomedical entity with a random entity mention and then querying the model with the correct entity mention in order to locate the corrupted part of the context. This de-noising task enables the model to learn good representations from abundant, unlabeled biomedical text that helps QA tasks and minimizes the train-test mismatch between the pre-training task and the downstream QA tasks by requiring the model to predict spans. Our experiments show that pre-training BioBERT on the proposed pre-training task significantly boosts performance and outperforms the previous best model from the 7th BioASQ Task 7b-Phase B challenge.

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Biomedical Question Answering via Weighted Neural Network Passage Retrieval

Galkó

Eickhoff

2018

Lecture Notes in Computer Science

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The amount of publicly available biomedical literature has been growing rapidly in recent years, yet question answering systems still struggle to exploit the full potential of this source of data. In a preliminary processing step, many question answering systems rely on retrieval models for identifying relevant documents and passages. This paper proposes a weighted cosine distance retrieval scheme based on neural network word embeddings. Our experiments are based on publicly available data and tasks from the BioASQ biomedical question answering challenge and demonstrate significant performance gains over a wide range of state-of-the-art models.

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KSAnswer: Question-answering System of Kangwon National University and Sogang University in the 2016 BioASQ Challenge

Cited by 5 publications

References 28 publications

Document Retrieval System for Biomedical Question Answering

Document Retrieval System for Biomedical Question Answering

Unsupervised Pre-training for Biomedical Question Answering

Biomedical Question Answering via Weighted Neural Network Passage Retrieval

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