scholarly journals Better synonyms for enriching biomedical search

2020 ◽  
Vol 27 (12) ◽  
pp. 1894-1902 ◽  
Author(s):  
Lana Yeganova ◽  
Sun Kim ◽  
Qingyu Chen ◽  
Grigory Balasanov ◽  
W John Wilbur ◽  
...  
Keyword(s):  
Gold Standard ◽  
Literature Search ◽  
Similarity Score ◽  
Isotonic Regression ◽  
Biomedical Literature ◽  
Cosine Similarity ◽  
Experimental Results ◽  
Word Embeddings ◽  
Embedding Method ◽  
Embedding Methods

Abstract Objective In a biomedical literature search, the link between a query and a document is often not established, because they use different terms to refer to the same concept. Distributional word embeddings are frequently used for detecting related words by computing the cosine similarity between them. However, previous research has not established either the best embedding methods for detecting synonyms among related word pairs or how effective such methods may be. Materials and Methods In this study, we first create the BioSearchSyn set, a manually annotated set of synonyms, to assess and compare 3 widely used word-embedding methods (word2vec, fastText, and GloVe) in their ability to detect synonyms among related pairs of words. We demonstrate the shortcomings of the cosine similarity score between word embeddings for this task: the same scores have very different meanings for the different methods. To address the problem, we propose utilizing pool adjacent violators (PAV), an isotonic regression algorithm, to transform a cosine similarity into a probability of 2 words being synonyms. Results Experimental results using the BioSearchSyn set as a gold standard reveal which embedding methods have the best performance in identifying synonym pairs. The BioSearchSyn set also allows converting cosine similarity scores into probabilities, which provides a uniform interpretation of the synonymy score over different methods. Conclusions We introduced the BioSearchSyn corpus of 1000 term pairs, which allowed us to identify the best embedding method for detecting synonymy for biomedical search. Using the proposed method, we created PubTermVariants2.0: a large, automatically extracted set of synonym pairs that have augmented PubMed searches since the spring of 2019.

scholarly journals Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Database ◽  
2019 ◽  
Vol 2019 ◽  
Author(s):  
Peter Brown ◽  
Aik-Choon Tan ◽  
Mohamed A El-Esawi ◽  
Thomas Liehr ◽  
Oliver Blanck ◽  
...  
Keyword(s):  
Literature Search ◽  
Relevant Literature ◽  
Biomedical Literature ◽  
Medical Subject Headings ◽  
Document Similarity ◽  
Inverse Document Frequency ◽  
Research Fields ◽  
Experience Levels ◽  
Document Frequency ◽  
Systematic Biases

Abstract Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.

scholarly journals GazeEMD: Detecting Visual Intention in Gaze-Based Human-Robot Interaction

Robotics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 68
Author(s):  
Lei Shi ◽  
Cosmin Copot ◽  
Steve Vanlanduit
Keyword(s):  
Cognitive Load ◽  
Real World ◽  
Robotic Manipulator ◽  
Similarity Score ◽  
Human Robot Interaction ◽  
Experimental Results ◽  
The Other ◽  
Robot Interaction ◽  
Run Length ◽  
The Real

In gaze-based Human-Robot Interaction (HRI), it is important to determine human visual intention for interacting with robots. One typical HRI interaction scenario is that a human selects an object by gaze and a robotic manipulator will pick up the object. In this work, we propose an approach, GazeEMD, that can be used to detect whether a human is looking at an object for HRI application. We use Earth Mover’s Distance (EMD) to measure the similarity between the hypothetical gazes at objects and the actual gazes. Then, the similarity score is used to determine if the human visual intention is on the object. We compare our approach with a fixation-based method and HitScan with a run length in the scenario of selecting daily objects by gaze. Our experimental results indicate that the GazeEMD approach has higher accuracy and is more robust to noises than the other approaches. Hence, the users can lessen cognitive load by using our approach in the real-world HRI scenario.

scholarly journals PubMed Labs: an experimental system for improving biomedical literature search

Database ◽  
2018 ◽  
Vol 2018 ◽  
Author(s):  
Nicolas Fiorini ◽  
Kathi Canese ◽  
Rostyslav Bryzgunov ◽  
Ievgeniia Radetska ◽  
Asta Gindulyte ◽  
...  
Keyword(s):  
Literature Search ◽  
Experimental System ◽  
Biomedical Literature

scholarly journals Linear Algebraic Structure of Word Senses, with Applications to Polysemy

2018 ◽  
Vol 6 ◽  
pp. 483-495 ◽  
Author(s):  
Sanjeev Arora ◽  
Yuanzhi Li ◽  
Yingyu Liang ◽  
Tengyu Ma ◽  
Andrej Risteski
Keyword(s):  
Information Retrieval ◽  
Random Walk ◽  
Algebraic Structure ◽  
Sparse Coding ◽  
Word Sense ◽  
Word Embeddings ◽  
Linear Superposition ◽  
Empirical Tests ◽  
Embedding Methods ◽  
Word Senses

Word embeddings are ubiquitous in NLP and information retrieval, but it is unclear what they represent when the word is polysemous. Here it is shown that multiple word senses reside in linear superposition within the word embedding and simple sparse coding can recover vectors that approximately capture the senses. The success of our approach, which applies to several embedding methods, is mathematically explained using a variant of the random walk on discourses model (Arora et al., 2016). A novel aspect of our technique is that each extracted word sense is accompanied by one of about 2000 “discourse atoms” that gives a succinct description of which other words co-occur with that word sense. Discourse atoms can be of independent interest, and make the method potentially more useful. Empirical tests are used to verify and support the theory.

scholarly journals Role of Laparoscopy in Management of Ectopic Pregnancy

2015 ◽  
Vol 5 (3) ◽  
pp. 170-174
Author(s):  
Hasina Banu ◽  
Ju Wen Hui ◽  
Liu Hua
Keyword(s):  
Ectopic Pregnancy ◽  
Gold Standard ◽  
Literature Search ◽  
Operating Time ◽  
Review Article ◽  
Laparoscopic Treatment ◽  
Gold Standard Method ◽  
Delay In Diagnosis ◽  
Future Fertility

Ectopic pregnancy means implantation of fertilized ovum outside the endometrial lining of the uterus. It remains the leading cause of early pregnancy-related death. Delay in diagnosis and treatment puts the life of women at risk. Laparoscopic surgery is increasingly becoming the preferred approach for ectopic pregnancy management. Laparoscopic treatment in ectopic pregnancy raises question of safety and feasibility when compared to laparotomy. In this review article our objective is to summarize the role of laparoscopy in management of ectopic pregnancy in comparison to laparotomy. For this, a literature search was done by using Google and PubMed. The selected articles were analyzed on laparoscopic treatment outcomes such as surgery success rate, operating time, intraoperative and postoperative complications, hospital stay, future fertility, postoperative recurrent ectopic pregnancy, cost-effectiveness in comparison to laparotomy. After analyzing all selected articles, it can be concluded that the laparoscopic management of ectopic pregnancy is safe, effective, and economical in comparision to laparotomy. So, for the patients’ benefit, laparoscopy should be considered as the gold standard method in management of ectopic pregnancy and is worthy to be popularized in clinical practice.J Enam Med Col 2015; 5(3): 170-174

Intelligent multi-document summarization for biomedical literature by word embeddings and graph-based ranking

2019 ◽  
Vol 37 (4) ◽  
pp. 4797-4802
Author(s):  
Chen Shen ◽  
Hongfei Lin ◽  
Huihui Hao ◽  
Zhihao Yang ◽  
Jian Wang ◽  
...  
Keyword(s):  
Biomedical Literature ◽  
Word Embeddings ◽  
Document Summarization

scholarly journals Open Problems in Indonesian Automatic Essay Scoring System

2018 ◽  
Vol 7 (4.44) ◽  
pp. 156
Author(s):  
Faisal Rahutomo ◽  
Trisna Ari Roshinta ◽  
Erfan Rohadi ◽  
Indrazno Siradjuddin ◽  
Rudy Ariyanto ◽  
...  
Keyword(s):  
Scoring System ◽  
Euclidean Distance ◽  
Learning Algorithm ◽  
Similarity Score ◽  
Cosine Similarity ◽  
Percentage Error ◽  
Similarity Metrics ◽  
Open Problems ◽  
Essay Scoring ◽  
Essay Grading

This paper presents open problems in Indonesian Scoring System. The previous study exposes the comparison of several similarity metrics on automated essay scoring in Indonesian. The metrics are Cosine Similarity, Euclidean Distance, and Jaccard. The data being used in the research are about 2,000 texts. This data are obtained from 50 students who answered 40 questions on politics, sports, lifestyle, and technology. The study also evaluates the stemming approach for the system performance. The difference between all methods between using stemming or not is around 4-9%. The results show Jaccard is the best metric both for the system with stemming or not. Jaccard method with stemming has the percentage error lowest than the others. The politic category has the highest average similarity score than lifestyle, sport, and technology. The percentage error of Jaccard with stemming is 52.31%, Cosine Similarity is 59.49%, and Euclidean Distance is 332.90%. In addition, Jaccard without stemming is also the best than the others. The percentage error without stemming of Jaccard is 56.05%, Cosine Similarity is 57.99%, and Euclidean Distance is 339.41%. However, this percentage error is high enough to be used for a functional essay grading system. The percentage errors are relatively high, more than 50%. Therefore this paper explores several ideas of open problems in this issue. The openly available dataset can be used to develop better approaches than the standard similarity metrics. The approaches expose are ranging from feature extraction, similarity metrics, learning algorithm, environment implementation, and performance evaluation.   

Correction Tower: A General Embedding Method of the Error Recognition for the Knowledge Graph Correction

2020 ◽  
Vol 34 (10) ◽  
pp. 2059034
Author(s):  
Farhad Abedini ◽  
Mohammad Reza Keyvanpour ◽  
Mohammad Bagher Menhaj
Keyword(s):  
Vector Space ◽  
Error Correction ◽  
Correction Method ◽  
The Other ◽  
Knowledge Graph ◽  
Embedding Method ◽  
Extraction Step ◽  
Error Recognition ◽  
Knowledge Graphs ◽  
Embedding Methods

Today, knowledge graphs (KGs) are growing by enrichment and refinement methods. The enrichment and refinement can be gained using the correction and completion of the KG. The studies of the KG completion are rich, but less attention has been paid to the methods of the KG error correction. The correction methods are divided into embedding and nonembedding methods. Embedding correction methods have been recently introduced in which a KG is embedded into a vector space. Also, existing correction approaches focused on the recognition of the three types of errors, the outliers, inconsistencies and erroneous relations. One of the challenges is that most outlier correction methods can recognize only numeric outlier entities by nonembedding methods. On the other hand, inconsistency errors are recognized during the knowledge extraction step and existing methods of this field do not pay attention to the recognition of these errors as post-correction by embedding methods. Also, to correct erroneous relations, new embedding techniques have not been used. Since the errors of a KG are variant and there is no method to cover all of them, a new general correction method is proposed in this paper. This method is called correction tower in which these three error types are corrected in three trays. In this correction tower, a new configuration will be suggested to solve the above challenges. For this aim, a new embedding method is proposed for each tray. Finally, the evaluation results show that the proposed correction tower can improve the KG error correction methods and proposed configuration can outperform previous results.

scholarly journals Combining entity co-occurrence with specialized word embeddings to measure entity relation in Alzheimer’s disease

2019 ◽  
Vol 19 (S5) ◽  
Author(s):  
Go Eun Heo ◽  
Qing Xie ◽  
Min Song ◽  
Jeong-Hoon Lee
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Language Processing ◽  
Modern Medicine ◽  
Biomedical Literature ◽  
Ranking Method ◽  
Superior Performance ◽  
Word Embeddings ◽  
Random Indexing ◽  
Meaningful Relationships

Abstract Background Extracting useful information from biomedical literature plays an important role in the development of modern medicine. In natural language processing, there have been rigorous attempts to find meaningful relationships between entities automatically by co-occurrence-based methods. It has been increasingly important to understand whether relationships exist, and if so how strong, between any two entities extracted from a large number of texts. One of the defining methods is to measure semantic similarity and relatedness between two entities. Methods We propose a hybrid ranking method that combines a co-occurrence approach considering both direct and indirect entity pair relationship with specialized word embeddings for measuring the relatedness of two entities. Results We evaluate the proposed ranking method comparatively with other well-known methods such as co-occurrence, Word2Vec, COALS (Correlated Occurrence Analog to Lexical Semantics), and random indexing by calculating top-ranked entities related to Alzheimer’s disease. In addition, we analyze gene, pathway, and gene–phenotype relationships. Overall, the proposed method tends to find more hidden relationships than the other methods. Conclusion Our proposed method is able to select more useful related entities that not only highly co-occur but also have more indirect relations for the target entity. In pathway analysis, our proposed method shows superior performance at identifying (functional) cross clustering and higher-level pathways. Our proposed method, resulting from phenotype analysis, has an advantage in identifying the common genotype relating to phenotypes from biological literature.

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