Results of the fifth edition of the BioASQ Challenge

Nentidis, Anastasios; Bougiatiotis, Konstantinos; Krithara, Anastasia; Παλιούρας, Γεώργιος; Kakadiaris, Ioannis A.

doi:10.18653/v1/w17-2306

Cited by 28 publications

(21 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our empirical performance evaluation is based on documents and questions from the BioASQ 2017 challenge's document and snippet retrieval tasks [7]. The goal in this task is to return the 10 most relevant passages from a collection of 12.8M PubMed abstracts for a specific biomedical question.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Biomedical Question Answering via Weighted Neural Network Passage Retrieval

Galkó

Eickhoff

2018

Lecture Notes in Computer Science

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

“…All (machine learning) methods are trained using five-fold cross validation on the training set. Word embeddings for all methods are computed as length-50 word2vec vectors on the PubMed document corpus [7]. For each question (training and test), a reference set of highly ranked documents is given by the challenge organizers.…”

Section: Methodsmentioning

confidence: 99%

Biomedical Question Answering via Weighted Neural Network Passage Retrieval

Galkó

Eickhoff

2018

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…We retrieved a maximum of 100 articles per question, ordered by the relevance score provided by the PubMed API, and the Dirichlet term smoothing score and BM25 score calculated by Galago. We retrieved 100 articles because this was the same number of articles retrieved by [19] (see Section IV-B). Then we compared with the articles given by the users that answered each question, which we considered as correct if they had more than a given number of votes.…”

Section: A Ir-based Validationmentioning

confidence: 99%

Generating Biomedical Question Answering Corpora From Q&A Forums

2020

View full text Add to dashboard Cite

Question Answering (QA) is a natural language processing task that aims at obtaining relevant answers to user questions. While some progress has been made in this area, biomedical questions are still a challenge to most QA approaches, due to the complexity of the domain and limited availability of training sets. We present a method to automatically extract question-article pairs from Q&A web forums, which can be used for document retrieval, a crucial step of most QA systems. The proposed framework extracts from selected forums the questions and the respective answers that contain citations. This way, QA systems based on document retrieval can be developed and evaluated using the question-article pairs annotated by users of these forums. We generated the BiQA corpus by applying our framework to three forums, obtaining 7,453 questions and 14,239 question-article pairs. We evaluated how the number of articles associated with each question and the number of votes on each answer affects the performance of baseline document retrieval approaches. Also, we demonstrated that the articles given as answers are significantly similar to the questions and trained a state-of-the-art deep learning model that obtained similar performance to using a dataset manually annotated by experts. The proposed framework can be used to update the BiQA corpus from the same forums as new posts are made, and from other forums that support their answers with documents. The BiQA corpus and the framework used to generate it are available at https://github.com/lasigeBioTM/BiQA.

show abstract

“…The multi-label PubMed article classification (Nentidis et al, 2017), helping the research community to solve this hard XMTC problem.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Deep Convolution Neural Network for Extreme Multi-label Text Classification

Gargiulo¹,

Silvestri²,

Ciampi³

2018

Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies

View full text Add to dashboard Cite

In this paper we present an analysis on the usage of Deep Neural Networks for extreme multi-label and multiclass text classification. We will consider two network models: the first one is formed by a word embeddings (WEs) stage followed by two dense layers, hereinafter Dense, and a second model with a convolution stage between the WEs and the dense layers, hereinafter CNN-Dense. We will take into account classification problems characterized by different number of labels, ranging from an order of 10 to an order of 30, 000, showing the different performances of the neural networks varying the total label number and the average number of labels for sample, exploiting the hierarchical structure of the label space of the dataset used for experimental assessment. It is worth noting that multi-label classification is an harder problem if compared to multi-class, due to the variable number of labels associated to each sample. We will even investigate on the behaviour of the neural networks as function of the training hyperparameters, analysing the link between them and the dataset complexity. All the result will be evaluated using the PubMed scientific articles collection as test case.

show abstract

Results of the fifth edition of the BioASQ Challenge

Cited by 28 publications

References 39 publications

Biomedical Question Answering via Weighted Neural Network Passage Retrieval

Biomedical Question Answering via Weighted Neural Network Passage Retrieval

Generating Biomedical Question Answering Corpora From Q&A Forums

Deep Convolution Neural Network for Extreme Multi-label Text Classification

Contact Info

Product

Resources

About