A Biomedical Question Answering System in BioASQ 2017

Sarrouti, Mourad; Alaoui, Said Ouatik El

doi:10.18653/v1/w17-2337

Cited by 19 publications

(12 citation statements)

References 17 publications

(17 reference statements)

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“…In this case, PHEN tags are still maintained. As another example, the entity 'drisapersen' with an O tag in the initial corpus is newly classified by 'B-CHEM 5 ' by the first classifier, the tag of the drisapersen is changed into 'CHEM', which is a chemical category of the UMLS semantic groups and includes drugs, proteins, steroids, vitamins, and others. In actual, the drisapersen is a type of drug.…”

Section: B the Bootstrapping To Improve The Automatically Generated mentioning

confidence: 99%

“…Biomedical named entity recognition(biomedical NER) task is defined as the identification of biomedical entities from the biomedical texts and their classification into categories such as disease, gene, protein, and drug. Since biomedical terms and their categories play an important role in many tasks of bioinformatics [1] such as relation extraction [2], [3], information retrieval [4], and question answering systems [5], many researchers have developed various methods for correctly extracting biomedical NEs. Rule-based approaches have been typically used to extract a biomedical NE [6], [7], while machine-learning based approaches have recently gained attention.…”

Section: Introductionmentioning

confidence: 99%

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A Bootstrapping Approach With CRF and Deep Learning Models for Improving the Biomedical Named Entity Recognition in Multi-Domains

Kim

Seo

2019

IEEE Access

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Biomedical named entity recognition (biomedical NER) is a core component to build biomedical text processing systems, such as biomedical information retrieval and question answering systems. Recently, many studies based on machine learning have been developed for a biomedical NER. The machine learning-based approaches generally require significant amounts of annotated corpora to achieve high performance. However, it is expensive to manually create a large number of high-quality corpora due to the demand for biomedical experts. In addition, most existing corpora have focused on several specific sub-domains, such as disease, protein, and species. It is difficult for a biomedical NER system trained with these corpora to provide much information for biomedical text processing systems. In this paper, we propose a method for automatically generating the machine-labeled biomedical NER corpus that covers various sub-domains by using proper categories from the semantic groups of a unified medical language system (UMLS). We use a bootstrapping approach with a small amount of manually annotated corpus to automatically generate a significant amount of corpus and then construct a biomedical NER system trained with the machine-labeled corpus. At last, we train two machine learning-based classifiers, conditional random fields (CRFs) and long short-term memory (LSTM), with the machine-labeled data to improve performance. The experimental results show that the proposed method is effective to improve performance. As a result, the proposed one obtains higher performance in 23.69% than the model that trained only a small amount of manually annotated corpus in F1-score.

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Section: B the Bootstrapping To Improve The Automatically Generated mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Bootstrapping Approach With CRF and Deep Learning Models for Improving the Biomedical Named Entity Recognition in Multi-Domains

Kim

Seo

2019

IEEE Access

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“…Sarrouti and El Alaoui [33] participated in fifth year BioASQ challenge. They simply used Metamap to identify named entities in text snippets and excluded entities which were also present in the question.…”

Section: Related Workmentioning

confidence: 99%

Multi-Label Question Classification for Factoid and List Type Questions in Biomedical Question Answering

et al. 2019

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Biomedical experts and bio-curators are unable to quickly find short and precise information using typical search engines as the amount of biomedical literature is increasing exponentially. The research community is focusing on biomedical question answering (QA) systems so that anyone can find precise information nuggets from the massive amount of biomedical literature. Generally, the user queries fall under different categories such as factoid, list, yes/no, or summary. The existing state-of-the-art question answering systems deal with most of these question types. However, the research to improve the performance of individual question types is also on the rise. To improve QA system performance, question classification plays a vital role for factoid and list type questions as it allows the answer processing stage to narrow down the candidate answer space and assigns a higher rank to the correct answers. A single biomedical answer or entity may be associated with more than one biomedical category or semantic type, e.g., Coenzyme Q(10) is classified under two categories in Unified Medical Language System (UMLS): organic chemical and biologically active substance. This inherent characteristic of biomedical entities makes question classification in the biomedical domain a multi-label classification problem, where one question might expect answers belonging to more than one semantic type. To the best of our knowledge, several QA systems deal with question classification as a multi-class classification problem and only one state-of-the-art system-OAQA-deals with it as a multi-label classification problem. In this paper, we analyze the pipeline of the OAQA system for factoid and list type questions, emphasizing the multi-label question classification. We use an improved question classification dataset with the copy transformation technique to improve the performance of list type questions. Moreover, we introduce a binary transformation in the pipeline of factoid questions to increase its performance. Our modified methodology enhances the performance of both list and factoid type questions by a margin of 2% and 3% evaluated on standard F 1 and Mean Reciprocal Rank measures, respectively.

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“…They report state of the art results on the Factoid and List type questions on the BioASQ dataset. Another prominent work is from Sarrouti and Alaoui (2017) who handle the generation of the exact answer type questions. They use a sentiment analysis based approach to answer the yes/no type questions making use of SentiWord-Net for the same.…”

Section: Relevant Literaturementioning

confidence: 99%

BioAMA: Towards an End to End BioMedical Question Answering System

Sharma¹,

Kulkarni²,

Pranavi³

et al. 2018

Proceedings of the BioNLP 2018 Workshop

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In this paper, we present a novel Biomedical Question Answering system, BioAMA: "Biomedical Ask Me Anything" on task 5b of the annual BioASQ challenge (Balikas et al., 2015). We focus on a wide variety of question types including factoid, list based, summary and yes/no type questions that generate both exact and wellformed 'ideal' answers. For summarytype questions, we combine effective IRbased techniques for retrieval and diversification of relevant snippets for a question to create an end-to-end system which achieves a ROUGE-2 score of 0.72 and a ROUGE-SU4 score of 0.71 on ideal answer questions (7% improvement over the previous best model). Additionally, we propose a novel Natural Language Inference (NLI) based framework to answer the yes/no questions. To train the NLI model, we also devise a transfer-learning technique by cross-domain projection of word embeddings. Finally, we present a two-stage approach to address the factoid and list type questions by first generating a candidate set using NER taggers and ranking them using both supervised and unsupervised techniques.

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A Biomedical Question Answering System in BioASQ 2017

Cited by 19 publications

References 17 publications

A Bootstrapping Approach With CRF and Deep Learning Models for Improving the Biomedical Named Entity Recognition in Multi-Domains

A Bootstrapping Approach With CRF and Deep Learning Models for Improving the Biomedical Named Entity Recognition in Multi-Domains

Multi-Label Question Classification for Factoid and List Type Questions in Biomedical Question Answering

BioAMA: Towards an End to End BioMedical Question Answering System

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