A Generic Document Retrieval Framework Based on UMLS Similarity for Biomedical Question Answering System

Sarrouti, Mourad; Alaoui, Said Ouatik El

doi:10.1007/978-3-319-39627-9_18

Cited by 8 publications

(2 citation statements)

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“…A method for document retrieval exploiting the UMLS semantic network was described in [163]. The method can be divided into two steps.…”

Section: Classical (Lexical or Rule Based) Methodsmentioning

confidence: 99%

Recent Advances in Automated Question Answering In Biomedical Domain

Baksi¹

2021

Preprint

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The objective of automated Question Answering (QA) systems is to provide answers to user queries in a time efficient manner. The answers are usually found in either databases (or knowledge bases) or a collection of documents commonly referred to as the corpus. In the past few decades there has been a proliferation of acquisition of knowledge and consequently there has been an exponential growth in new scientific articles in the field of biomedicine. Therefore, it has become difficult to keep track of all the information in the domain, even for domain experts. With the improvements in commercial search engines, users can type in their queries and get a small set of documents most relevant for answering their query, as well as relevant snippets from the documents in some cases. However, it may be still tedious and time consuming to manually look for the required information or answers. This has necessitated the development of efficient QA systems which aim to find exact and precise answers to user provided natural language questions in the domain of biomedicine. In this paper, we introduce the basic methodologies used for developing general domain QA systems, followed by a thorough investigation of different aspects of biomedical QA systems, including benchmark datasets and several proposed approaches, both using structured databases and collection of texts. We also explore the limitations of current systems and explore potential avenues for further advancement.

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“…A method for document retrieval exploiting the UMLS semantic network was described in [163]. The method can be divided into two steps.…”

Section: Classical (Lexical or Rule Based) Methodsmentioning

confidence: 99%

Recent Advances in Automated Question Answering In Biomedical Domain

Baksi¹

2021

Preprint

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“…As a response to such growth of data, several tasks have been posed in order to analyse these biomedical data. These tasks such as Biomedical Question Answering which aims to identify a precise answer for a biomedical question [2], and Indexing Biomedical Documents which aims to classify the biomedical documents into its category [3]. In order to perform these tasks, it is necessary to accommodate a prior process of recognition for the biomedical entities.…”

Section: Introductionmentioning

confidence: 99%

Biomedical Named Entity Recognition: A Review

Alshaikhdeeb

Ahmad

2016

International Journal on Advanced Science, Engineering and Information Technology

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Biomedical Named Entity Recognition (BNER) is the task of identifying biomedical instances such as chemical compounds, genes, proteins, viruses, disorders, DNAs and RNAs. The key challenge behind BNER lies on the methods that would be used for extracting such entities. Most of the methods used for BNER were relying on Supervised Machine Learning (SML) techniques. In SML techniques, the features play an essential role in terms of improving the effectiveness of the recognition process. Features can be identified as a set of discriminating and distinguishing characteristics that have the ability to indicate the occurrence of an entity. In this manner, the features should be able to generalize which means to discriminate the entities correctly even on new and unseen samples. Several studies have tackled the role of features in terms of identifying named entities. However, with the surge of biomedical researches, there is a vital demand to explore biomedical features. This paper aims to accommodate a review study on the features that could be used for BNER in which various types of features will be examined including morphological features, dictionary-based features, lexical features and distance-based features.

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