scholarly journals Named Entity Recognition in Natural Language Texts obtained through Audio Interfaces

2017 ◽  
Author(s):  
Nail Gafurov ◽  
Alexey Platonov
Keyword(s):  
Natural Language ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Audio Interfaces

scholarly journals Obtaining Knowledge in Pathology Reports Through a Natural Language Processing Approach With Classification, Named-Entity Recognition, and Relation-Extraction Heuristics

2019 ◽  
pp. 1-8 ◽  
Author(s):  
Tomasz Oliwa ◽  
Steven B. Maron ◽  
Leah M. Chase ◽  
Samantha Lomnicki ◽  
Daniel V.T. Catenacci ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Named Entity ◽  
Pathology Reports

PURPOSE Robust institutional tumor banks depend on continuous sample curation or else subsequent biopsy or resection specimens are overlooked after initial enrollment. Curation automation is hindered by semistructured free-text clinical pathology notes, which complicate data abstraction. Our motivation is to develop a natural language processing method that dynamically identifies existing pathology specimen elements necessary for locating specimens for future use in a manner that can be re-implemented by other institutions. PATIENTS AND METHODS Pathology reports from patients with gastroesophageal cancer enrolled in The University of Chicago GI oncology tumor bank were used to train and validate a novel composite natural language processing-based pipeline with a supervised machine learning classification step to separate notes into internal (primary review) and external (consultation) reports; a named-entity recognition step to obtain label (accession number), location, date, and sublabels (block identifiers); and a results proofreading step. RESULTS We analyzed 188 pathology reports, including 82 internal reports and 106 external consult reports, and successfully extracted named entities grouped as sample information (label, date, location). Our approach identified up to 24 additional unique samples in external consult notes that could have been overlooked. Our classification model obtained 100% accuracy on the basis of 10-fold cross-validation. Precision, recall, and F1 for class-specific named-entity recognition models show strong performance. CONCLUSION Through a combination of natural language processing and machine learning, we devised a re-implementable and automated approach that can accurately extract specimen attributes from semistructured pathology notes to dynamically populate a tumor registry.

Probing Patient Messages Enhanced by Natural Language Processing: A Top-Down Message Corpus Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
George Mastorakos ◽  
Aditya Khurana ◽  
Ming Huang ◽  
Sunyang Fu ◽  
Ahmad P. Tafti ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Corpus Analysis ◽  
Entity Recognition ◽  
Message Content ◽  
Named Entity ◽  
Medical Concepts ◽  
Insight Into

Background. Patients increasingly use asynchronous communication platforms to converse with care teams. Natural language processing (NLP) to classify content and automate triage of these messages has great potential to enhance clinical efficiency. We characterize the contents of a corpus of portal messages generated by patients using NLP methods. We aim to demonstrate descriptive analyses of patient text that can contribute to the development of future sophisticated NLP applications. Methods. We collected approximately 3,000 portal messages from the cardiology, dermatology, and gastroenterology departments at Mayo Clinic. After labeling these messages as either Active Symptom, Logistical, Prescription, or Update, we used NER (named entity recognition) to identify medical concepts based on the UMLS library. We hierarchically analyzed the distribution of these messages in terms of departments, message types, medical concepts, and keywords therewithin. Results. Active Symptom and Logistical content types comprised approximately 67% of the message cohort. The “Findings” medical concept had the largest number of keywords across all groupings of content types and departments. “Anatomical Sites” and “Disorders” keywords were more prevalent in Active Symptom messages, while “Drugs” keywords were most prevalent in Prescription messages. Logistical messages tended to have the lower proportions of “Anatomical Sites,”, “Disorders,”, “Drugs,”, and “Findings” keywords when compared to other message content types. Conclusions. This descriptive corpus analysis sheds light on the content and foci of portal messages. The insight into the content and differences among message themes can inform the development of more robust NLP models.

Advances in Computational Linguistics and Text Processing Frameworks

2020 ◽  
pp. 217-244
Author(s):  
Ayush Srivastav ◽  
Hera Khan ◽  
Amit Kumar Mishra
Keyword(s):  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Text Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Part Of Speech

The chapter provides an eloquent account of the major methodologies and advances in the field of Natural Language Processing. The most popular models that have been used over time for the task of Natural Language Processing have been discussed along with their applications in their specific tasks. The chapter begins with the fundamental concepts of regex and tokenization. It provides an insight to text preprocessing and its methodologies such as Stemming and Lemmatization, Stop Word Removal, followed by Part-of-Speech tagging and Named Entity Recognition. Further, this chapter elaborates the concept of Word Embedding, its various types, and some common frameworks such as word2vec, GloVe, and fastText. A brief description of classification algorithms used in Natural Language Processing is provided next, followed by Neural Networks and its advanced forms such as Recursive Neural Networks and Seq2seq models that are used in Computational Linguistics. A brief description of chatbots and Memory Networks concludes the chapter.

scholarly journals OryzaGP 2021 update: a rice gene and protein dataset for named-entity recognition

2021 ◽  
Vol 19 (3) ◽  
pp. e27
Author(s):  
Pierre Larmande ◽  
Yusha Liu ◽  
Xinzhi Yao ◽  
Jingbo Xia
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Rapid Evolution ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Biological Databases ◽  
Named Entity ◽  
Biological Domain ◽  
Or Gene ◽  
Protein Dataset

Due to the rapid evolution of high-throughput technologies, a tremendous amount of data is being produced in the biological domain, which poses a challenging task for information extraction and natural language understanding. Biological named entity recognition (NER) and named entity normalisation (NEN) are two common tasks aiming at identifying and linking biologically important entities such as genes or gene products mentioned in the literature to biological databases. In this paper, we present an updated version of OryzaGP, a gene and protein dataset for rice species created to help natural language processing (NLP) tools in processing NER and NEN tasks. To create the dataset, we selected more than 15,000 abstracts associated with articles previously curated for rice genes. We developed four dictionaries of gene and protein names associated with database identifiers. We used these dictionaries to annotate the dataset. We also annotated the dataset using pre-trained NLP models. Finally, we analysed the annotation results and discussed how to improve OryzaGP.

scholarly journals DeNERT-KG: Named Entity and Relation Extraction Model Using DQN, Knowledge Graph, and BERT

2020 ◽  
Vol 10 (18) ◽  
pp. 6429
Author(s):  
SungMin Yang ◽  
SoYeop Yoo ◽  
OkRan Jeong
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Language Model ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
Entity Recognition ◽  
Knowledge Graph ◽  
Named Entity ◽  
Artificial Intelligence Technology

Along with studies on artificial intelligence technology, research is also being carried out actively in the field of natural language processing to understand and process people’s language, in other words, natural language. For computers to learn on their own, the skill of understanding natural language is very important. There are a wide variety of tasks involved in the field of natural language processing, but we would like to focus on the named entity registration and relation extraction task, which is considered to be the most important in understanding sentences. We propose DeNERT-KG, a model that can extract subject, object, and relationships, to grasp the meaning inherent in a sentence. Based on the BERT language model and Deep Q-Network, the named entity recognition (NER) model for extracting subject and object is established, and a knowledge graph is applied for relation extraction. Using the DeNERT-KG model, it is possible to extract the subject, type of subject, object, type of object, and relationship from a sentence, and verify this model through experiments.

scholarly journals EXSEQREG: Explaining sequence-based NLP tasks with regions with a case study using morphological features for named entity recognition

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244179
Author(s):  
Onur Güngör ◽  
Tunga Güngör ◽  
Suzan Uskudarli
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Language Engineering ◽  
Named Entity ◽  
Prediction Probability ◽  
Information Score ◽  
Input Sentence ◽  
The Impact

The state-of-the-art systems for most natural language engineering tasks employ machine learning methods. Despite the improved performances of these systems, there is a lack of established methods for assessing the quality of their predictions. This work introduces a method for explaining the predictions of any sequence-based natural language processing (NLP) task implemented with any model, neural or non-neural. Our method named EXSEQREG introduces the concept of region that links the prediction and features that are potentially important for the model. A region is a list of positions in the input sentence associated with a single prediction. Many NLP tasks are compatible with the proposed explanation method as regions can be formed according to the nature of the task. The method models the prediction probability differences that are induced by careful removal of features used by the model. The output of the method is a list of importance values. Each value signifies the impact of the corresponding feature on the prediction. The proposed method is demonstrated with a neural network based named entity recognition (NER) tagger using Turkish and Finnish datasets. A qualitative analysis of the explanations is presented. The results are validated with a procedure based on the mutual information score of each feature. We show that this method produces reasonable explanations and may be used for i) assessing the degree of the contribution of features regarding a specific prediction of the model, ii) exploring the features that played a significant role for a trained model when analyzed across the corpus.

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