A rule-based named-entity recognition method for knowledge extraction of evidence-based dietary recommendations

Eftimov, Tome; Seljak, Barbara Koroušić; Korošec, Peter

doi:10.1371/journal.pone.0179488

Cited by 115 publications

(68 citation statements)

References 54 publications

(30 reference statements)

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“…The terms are tagged with respective classes using the SGML (Standard Generalized Markup Language) format. Recently, however, there is not much literature on pure handcrafted rule-based BioNER systems, and instead, papers such as Wei et al ( 2012 ) and Eftimov et al ( 2017 ) present how combining heuristic rules with dictionaries may result in higher state-of-the-art f-scores. The two techniques complement each other by rules compensating for exact dictionary matches, and dictionaries refining results extracted through rules.…”

Section: Biomedical Named Entity Recognition (Bioner)mentioning

confidence: 99%

Named Entity Recognition and Relation Detection for Biomedical Information Extraction

Perera

Dehmer

Emmert‐Streib

2020

Front. Cell Dev. Biol.

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The number of scientific publications in the literature is steadily growing, containing our knowledge in the biomedical, health, and clinical sciences. Since there is currently no automatic archiving of the obtained results, much of this information remains buried in textual details not readily available for further usage or analysis. For this reason, natural language processing (NLP) and text mining methods are used for information extraction from such publications. In this paper, we review practices for Named Entity Recognition (NER) and Relation Detection (RD), allowing, e.g., to identify interactions between proteins and drugs or genes and diseases. This information can be integrated into networks to summarize large-scale details on a particular biomedical or clinical problem, which is then amenable for easy data management and further analysis. Furthermore, we survey novel deep learning methods that have recently been introduced for such tasks.

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Section: Biomedical Named Entity Recognition (Bioner)mentioning

confidence: 99%

Named Entity Recognition and Relation Detection for Biomedical Information Extraction

Perera

Dehmer

Emmert‐Streib

2020

Front. Cell Dev. Biol.

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“…For example, Eftimov et al [19] built a set of regular expressions to extract evidence-based dietary recommendations from scientific publications and websites. They first detected target mentions in textual data and then extracted them using the rule-based technique.…”

Section: Biomedical Entity Extractionmentioning

confidence: 99%

Testing Contextualized Word Embeddings to Improve NER in Spanish Clinical Case Narratives

Akhtyamova

Martı́nez

Verspoor

et al. 2020

Preprint

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Background: In the Big Data era there is an increasing need to fully exploit and analyse the huge quantity of information available about health. Natural Language Processing (NLP) technologies can contribute to extract relevant information from unstructured data contained in Electronic Health Records (EHR) such as clinical notes, patient’s discharge summaries and radiology reports among others. Extracted information could help in health-related decision making processes. Named entity recognition (NER) devoted to detect important concepts in texts (diseases, symptoms, drugs, etc.) is a crucial task in information extraction processes especially in languages other than English. In this work, we develop a deep learning-based NLP pipeline for biomedical entity extraction in Spanish clinical narrative. Methods: We explore the use of contextualized word embeddings to enhance named entity recognition in Spanish language clinical text, particularly of pharmacological substances, compounds, and proteins. Various combinations of word and sense embeddings were tested on the evaluation corpus of the PharmacoNER 2019 task, the Spanish Clinical Case Corpus (SPACCC). This data set consists of clinical case sections derived from open access Spanish-language medical publications. Results: NER system integrates in-domain pre-trained Flair and FastText word embeddings, byte-pairwise encoded and the bi-LSTM-based character word embeddings. The system yielded the best performance measure with F-score of 90.84%. Error analysis showed that the main source of errors for the best model is the newly detected false positive entities with the half of that amount of errors belonged to longer than the actual ones detected entities. Conclusions: Our study shows that our deep-learning-based system with domain-specific contextualized embeddings coupled with stacking of complementary embeddings yields superior performance over the system with integrated standard and general-domain word embeddings. With this system, we achieve performance competitive with the state-of-the-art.

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“…In the dictionary-based approach, a finite set of named entities are stored in a lexicon which acts as a look-up table to identify the entities in a text document [28,29]. Rule-based systems employ hand-crafted rules for identifying named entities from unstructured text [30,31]. They are designed for specific domains like clinical text, medical reports and so forth and are specific to the languages for which the rules are written.…”

Section: Related Workmentioning

confidence: 99%

An Improved Word Representation for Deep Learning Based NER in Indian Languages

2019

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Named Entity Recognition (NER) is the process of identifying the elementary units in a text document and classifying them into predefined categories such as person, location, organization and so forth. NER plays an important role in many Natural Language Processing applications like information retrieval, question answering, machine translation and so forth. Resolving the ambiguities of lexical items involved in a text document is a challenging task. NER in Indian languages is always a complex task due to their morphological richness and agglutinative nature. Even though different solutions were proposed for NER, it is still an unsolved problem. Traditional approaches to Named Entity Recognition were based on the application of hand-crafted features to classical machine learning techniques such as Hidden Markov Model (HMM), Support Vector Machine (SVM), Conditional Random Field (CRF) and so forth. But the introduction of deep learning techniques to the NER problem changed the scenario, where the state of art results have been achieved using deep learning architectures. In this paper, we address the problem of effective word representation for NER in Indian languages by capturing the syntactic, semantic and morphological information. We propose a deep learning based entity extraction system for Indian languages using a novel combined word representation, including character-level, word-level and affix-level embeddings. We have used ‘ARNEKT-IECSIL 2018’ shared data for training and testing. Our results highlight the improvement that we obtained over the existing pre-trained word representations.

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A rule-based named-entity recognition method for knowledge extraction of evidence-based dietary recommendations

Cited by 115 publications

References 54 publications

Named Entity Recognition and Relation Detection for Biomedical Information Extraction

Named Entity Recognition and Relation Detection for Biomedical Information Extraction

Testing Contextualized Word Embeddings to Improve NER in Spanish Clinical Case Narratives

An Improved Word Representation for Deep Learning Based NER in Indian Languages

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