Word sense disambiguation across two domains: Biomedical literature and clinical notes

Savova, Guergana; Coden, Anni; Sominsky, Igor L.; Johnson, Rie; Ogren, Philip V.; Groen, Piet C. de; Chute, Christopher G.

doi:10.1016/j.jbi.2008.02.003

Cited by 57 publications

(31 citation statements)

References 14 publications

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“…Besides the confusions in WSD, there many difficulties in handling these using the supervised and unsupervised methods. Work in [34] determined that supervised methods are the optimal predictors of WSD difficulties, but are limited by their dependence on labelled training data in different domain types such as bionadical [35], [36]. The unsupervised method performed well in some situations and can be applied more broadly [37], [38].…”

Section: Related Workmentioning

confidence: 99%

Semi Supervised Method for Detection of Ambiguous Word and Creation of Sense: Using WordNet

Saqib¹,

Kundi²,

Syed³

et al. 2018

ijacsa

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Machine Translation, Information Retrieval and Knowledge Acquisition are the three main applications of Word Sense Disambiguation (WSD). The sense of a target word can be identified from a dictionary using a 'bag of words', i.e. neighbours of the target word. A target word has the same spelling of the word but with a different meaning, i.e. chair, light etc. In WSD, the key input sources are sentences and target words. But, instead of providing a target word, this should automatically be detected. If a sentence has more than one target word, then the filtration process will require further processing. In this study, the proposed framework, consisting of buzz words and query words has been developed to detect target words using the WordNet dictionary. Buzz words are defined as a 'bag-ofwords' using POS-Tags, and query words are those words having multiple meanings. The proposed framework will endeavor to find the sense of the detected target word using its gloss and with examples containing buzz words. This is a semi-supervised approach because 266 words of multiple meanings have been labelled from various sources and used based on an unsupervised approach to detect the target word and sense (meaning). After experimenting on a dataset consisting of 300 hotel reviews, 100 % of the target words for each sentence were detected with 84 % related to the sense of each sentence or phrase.

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Section: Related Workmentioning

confidence: 99%

Semi Supervised Method for Detection of Ambiguous Word and Creation of Sense: Using WordNet

Saqib¹,

Kundi²,

Syed³

et al. 2018

ijacsa

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“…The primary feature was Unified Medical Language System (UMLS) concept unique identifiers (CUIs), which represented clinical concepts in the patient's EHR notes. The CUIs were extracted using Apache cTAKES [36] and converted into concept vectors. cTAKES implements a full stack of NLP modules including part-of-speech tagger, parsers, relation discovery modules, as well as attribute identification modules (such as negation, uncertainty, subject).…”

Section: Automated Algorithms For Seco Detectionmentioning

confidence: 99%

Developing an Algorithm to Detect Early Childhood Obesity in Two Tertiary Pediatric Medical Centers

Lingren¹,

Thaker²,

Brady³

et al. 2016

Appl Clin Inform

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SummaryObjective: The objective of this study is to develop an algorithm to accurately identify children with severe early onset childhood obesity (ages 1-5.99 years) using structured and unstructured data from the electronic health record (EHR). Introduction: Childhood obesity increases risk factors for cardiovascular morbidity and vascular disease. Accurate definition of a high precision phenotype through a standardize tool is critical to the success of large-scale genomic studies and validating rare monogenic variants causing severe early onset obesity. Data and Methods: Rule based and machine learning based algorithms were developed using structured and unstructured data from two EHR databases from Boston Children's Hospital (BCH) and Cincinnati Children's Hospital and Medical Center (CCHMC). Exclusion criteria including medications or comorbid diagnoses were defined. Machine learning algorithms were developed using cross-site training and testing in addition to experimenting with natural language processing features. Results: Precision was emphasized for a high fidelity cohort. The rule-based algorithm performed the best overall, 0.895 (CCHMC) and 0.770 (BCH). The best feature set for machine learning employed Unified Medical Language System (UMLS) concept unique identifiers (CUIs), ICD-9 codes, and RxNorm codes. Conclusions: Detecting severe early childhood obesity is essential for the intervention potential in children at the highest long-term risk of developing comorbidities related to obesity and excluding patients with underlying pathological and non-syndromic causes of obesity assists in developing a high-precision cohort for genetic study. Further such phenotyping efforts inform future practical application in health care environments utilizing clinical decision support. Research Article

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“…Most of the methods for biomedical entity name recognition, classification, or disambiguation can be roughly divided into three categories: (i) supervised and machine-learning-based techniques, (ii) statistical and corpus-based techniques, and (iii) syntactic and rule-based techniques [9–11]. Moreover, the bioinformatics literature shows that biomedical WSD has been a quite active area of research with a number of approaches proposed and applied to biomedical data [1, 2, 4, 8, 12, 13]. …”

Section: Related Workmentioning

confidence: 99%

“…The supervised methods rely on training and learning phases that require a dataset or corpus containing manually disambiguated instances to be used to train the system [5, 6]. The unsupervised methods, on the other hand, are based on knowledge sources like ontology, for example, from UMLS, or text corpora [2, 4, 7, 8]. Our approach in this paper is a supervised approach.…”

Section: Introductionmentioning

confidence: 99%

A Learning-Based Approach for Biomedical Word Sense Disambiguation

Al-Mubaid

Gungu

2012

The Scientific World Journal

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In the biomedical domain, word sense ambiguity is a widely spread problem with bioinformatics research effort devoted to it being not commensurate and allowing for more development. This paper presents and evaluates a learning-based approach for sense disambiguation within the biomedical domain. The main limitation with supervised methods is the need for a corpus of manually disambiguated instances of the ambiguous words. However, the advances in automatic text annotation and tagging techniques with the help of the plethora of knowledge sources like ontologies and text literature in the biomedical domain will help lessen this limitation. The proposed method utilizes the interaction model (mutual information) between the context words and the senses of the target word to induce reliable learning models for sense disambiguation. The method has been evaluated with the benchmark dataset NLM-WSD with various settings and in biomedical entity species disambiguation. The evaluation results showed that the approach is very competitive and outperforms recently reported results of other published techniques.

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Word sense disambiguation across two domains: Biomedical literature and clinical notes

Cited by 57 publications

References 14 publications

Semi Supervised Method for Detection of Ambiguous Word and Creation of Sense: Using WordNet

Semi Supervised Method for Detection of Ambiguous Word and Creation of Sense: Using WordNet

Developing an Algorithm to Detect Early Childhood Obesity in Two Tertiary Pediatric Medical Centers

A Learning-Based Approach for Biomedical Word Sense Disambiguation

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