Towards understanding word embeddings: Automatically explaining similarity of terms

Zhang, Yating; Jatowt, Adam; Tanaka, Katsumi

doi:10.1109/bigdata.2016.7840675

Cited by 16 publications

(8 citation statements)

References 25 publications

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“…Given a keyword, for example, the name of a drug, this method formulated a feature vector that best predicts a window of surrounding words that occur in some meaningful context. Such semantic similarity also conforms to the important criteria for selecting good word pairs [23].…”

Section: Experimental Designmentioning

confidence: 56%

Drug prescription support in dental clinics through drug corpus mining

Goh

Tao

et al. 2018

Int J Data Sci Anal

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The rapid increase in the volume and variety of data poses a challenge to safe drug prescription for the dentist. The increasing number of patients that take multiple drugs further exerts pressure on the dentist to make the right decision at point-of-care. Hence, a robust decision support system will enable dentists to make decisions on drug prescription quickly and accurately. Based on the assumption that similar drug-pairs have a higher similarity ratio, this paper suggests an innovative approach to obtain the similarity ratio between the drug that the dentist is going to prescribe and the drug that the patient is currently taking. We conducted experiments to obtain the similarity ratios of both positive and negative drug-pairs, by using feature vectors generated from term similarities and word embeddings of bio-medical text corpus. This model can be easily adapted and implemented for use in a dental clinic to assist the dentist in deciding if a drug is suitable for prescription, taking into consideration the medical profile of the patients. Experimental evaluation of our model's association of the similarity ratio between two drugs yielded a superior F score of 89%.Hence, such an approach, when integrated within the clinical work-flow, will reduce prescription errors and thereby increase the health outcomes of patients.

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Section: Experimental Designmentioning

confidence: 56%

Drug prescription support in dental clinics through drug corpus mining

Goh

Tao

et al. 2018

Int J Data Sci Anal

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“…Given a keyword, for example, the drug name, this method formulated a feature vector that best predicts a window of surrounding words that occur in some meaningful context. Such semantic similarity also conforms to the important criteria for selecting good word pairs ( [34]) When training the dataset, the parameters required by word2Vec were the word frequency (the minimum number of times a word must appear in the corpus), layer size (the number of desired features in the word vector) and window size (the number of words before and after the word to extract for the training sample).…”

Section: Word Embedding Modelmentioning

confidence: 86%

Feature-Based Learning in Drug Prescription System for Medical Clinics

Goh

Tao

Zhang

et al. 2020

Neural Process Lett

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Rapid increases in data volume and variety pose a challenge to safe drug prescription for health professionals like doctors and dentists. This is addressed by our study, which presents innovative approaches in mining data from drug corpus and extracting feature vectors to combine this knowledge with individual patient medical profiles. Within our three-tiered frameworkthe prediction layer, the knowledge layer and the presentation layer-we describe multiple approaches in computing similarity ratios from the feature vectors, illustrated with an example of applying the framework in a typical medical clinic. Experimental evaluation shows that the word embedding model performs better than the adverse network model, with a F score of 0.75. The F score is a common metrics used for evaluating the performance of classification algorithms. Similarity to a drug the patient is allergic to or is taking are important considerations for the suitability of a drug for prescription. Hence, such an approach, when integrated within the clinical work-flow, will reduce prescription errors thereby increasing patient health outcomes. Keywords Feature vector • Similarity ratio • Word embedding • Adverse network model • Personalised drug prescription This work is partially supported by Glory Dental Surgery Pte Ltd and undertaken collaboratively with their panel of dentists. We would like to thank Dr. Xueling Oh and Dr. Elizabeth Goh for enriching the authors understanding of drug prescription within the medical domain.

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“…Recent work suggests that intrinsic and extrinsic measures correlate poorly with one another (Schnabel et al, 2015;Gladkova and Drozd, 2016;Zhang et al, 2016). In many cases we want an embedding not just to capture relationships within the data, but also to do so in a way which can be usefully applied.…”

Section: Embedding Evaluationmentioning

confidence: 99%

ESTEEM: A Novel Framework for Qualitatively Evaluating and Visualizing Spatiotemporal Embeddings in Social Media

Arendt¹,

Volkova²

2017

Proceedings of ACL 2017, System Demonstrations

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Analyzing and visualizing large amounts of social media communications and contrasting short-term conversation changes over time and geolocations is extremely important for commercial and government applications. Earlier approaches for largescale text stream summarization used dynamic topic models and trending words. Instead, we rely on text embeddingslow-dimensional word representations in a continuous vector space where similar words are embedded nearby each other. This paper presents ESTEEM, 1 a novel tool for visualizing and evaluating spatiotemporal embeddings learned from streaming social media texts. Our tool allows users to monitor and analyze query words and their closest neighbors with an interactive interface. We used stateof-the-art techniques to learn embeddings and developed a visualization to represent dynamically changing relations between words in social media over time and other dimensions. This is the first interactive visualization of streaming text representations learned from social media texts that also allows users to contrast differences across multiple dimensions of the data.

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Towards understanding word embeddings: Automatically explaining similarity of terms

Cited by 16 publications

References 25 publications

Drug prescription support in dental clinics through drug corpus mining

Drug prescription support in dental clinics through drug corpus mining

Feature-Based Learning in Drug Prescription System for Medical Clinics

ESTEEM: A Novel Framework for Qualitatively Evaluating and Visualizing Spatiotemporal Embeddings in Social Media

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