scholarly journals Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language

1999 ◽  
Vol 11 ◽  
pp. 95-130 ◽  
Author(s):  
P. Resnik
Keyword(s):  
Natural Language ◽  
Information Content ◽  
Semantic Similarity ◽  
Experimental Evaluation ◽  
Experimental Results ◽  
Semantic Ambiguity ◽  
Counting Approach ◽  
Shared Information ◽  
Similarity Judgments ◽  
Better Than

This article presents a measure of semantic similarity in an IS-A taxonomy based on the notion of shared information content. Experimental evaluation against a benchmark set of human similarity judgments demonstrates that the measure performs better than the traditional edge-counting approach. The article presents algorithms that take advantage of taxonomic similarity in resolving syntactic and semantic ambiguity, along with experimental results demonstrating their effectiveness.

scholarly journals GO2Vec: transforming GO terms and proteins to vector representations via graph embeddings

BMC Genomics ◽  
2019 ◽  
Vol 20 (S9) ◽  
Author(s):  
Xiaoshi Zhong ◽  
Rama Kaalia ◽  
Jagath C. Rajapakse
Keyword(s):  
Information Content ◽  
Semantic Similarity ◽  
Protein Interactions ◽  
Large Scale ◽  
Functional Similarity ◽  
Experimental Results ◽  
Graph Embeddings ◽  
Protein Protein Interactions ◽  
Vector Representations ◽  
Go Terms

Abstract Background Semantic similarity between Gene Ontology (GO) terms is a fundamental measure for many bioinformatics applications, such as determining functional similarity between genes or proteins. Most previous research exploited information content to estimate the semantic similarity between GO terms; recently some research exploited word embeddings to learn vector representations for GO terms from a large-scale corpus. In this paper, we proposed a novel method, named GO2Vec, that exploits graph embeddings to learn vector representations for GO terms from GO graph. GO2Vec combines the information from both GO graph and GO annotations, and its learned vectors can be applied to a variety of bioinformatics applications, such as calculating functional similarity between proteins and predicting protein-protein interactions. Results We conducted two kinds of experiments to evaluate the quality of GO2Vec: (1) functional similarity between proteins on the Collaborative Evaluation of GO-based Semantic Similarity Measures (CESSM) dataset and (2) prediction of protein-protein interactions on the Yeast and Human datasets from the STRING database. Experimental results demonstrate the effectiveness of GO2Vec over the information content-based measures and the word embedding-based measures. Conclusion Our experimental results demonstrate the effectiveness of using graph embeddings to learn vector representations from undirected GO and GOA graphs. Our results also demonstrate that GO annotations provide useful information for computing the similarity between GO terms and between proteins.

scholarly journals DIBERT: Dependency Injected Bidirectional Encoder Representations from Transformers

2021 ◽  
Author(s):  
Abdul Wahab ◽  
Rafet Sifa
Keyword(s):  
Natural Language ◽  
Sentiment Analysis ◽  
Semantic Similarity ◽  
Syntactic Structure ◽  
Language Modeling ◽  
Benchmark Dataset ◽  
Fine Tuning ◽  
New Model ◽  
Dependency Tree ◽  
Better Than

<div> <div> <div> <p> </p><div> <div> <div> <p>In this paper, we propose a new model named DIBERT which stands for Dependency Injected Bidirectional Encoder Representations from Transformers. DIBERT is a variation of the BERT and has an additional third objective called Parent Prediction (PP) apart from Masked Language Modeling (MLM) and Next Sentence Prediction (NSP). PP injects the syntactic structure of a dependency tree while pre-training the DIBERT which generates syntax-aware generic representations. We use the WikiText-103 benchmark dataset to pre-train both BERT- Base and DIBERT. After fine-tuning, we observe that DIBERT performs better than BERT-Base on various downstream tasks including Semantic Similarity, Natural Language Inference and Sentiment Analysis. </p> </div> </div> </div> </div> </div> </div>

scholarly journals DIBERT: Dependency Injected Bidirectional Encoder Representations from Transformers

2021 ◽  
Author(s):  
Abdul Wahab ◽  
Rafet Sifa
Keyword(s):  
Natural Language ◽  
Sentiment Analysis ◽  
Semantic Similarity ◽  
Syntactic Structure ◽  
Language Modeling ◽  
Benchmark Dataset ◽  
Fine Tuning ◽  
New Model ◽  
Dependency Tree ◽  
Better Than

<div> <div> <div> <p> </p><div> <div> <div> <p>In this paper, we propose a new model named DIBERT which stands for Dependency Injected Bidirectional Encoder Representations from Transformers. DIBERT is a variation of the BERT and has an additional third objective called Parent Prediction (PP) apart from Masked Language Modeling (MLM) and Next Sentence Prediction (NSP). PP injects the syntactic structure of a dependency tree while pre-training the DIBERT which generates syntax-aware generic representations. We use the WikiText-103 benchmark dataset to pre-train both BERT- Base and DIBERT. After fine-tuning, we observe that DIBERT performs better than BERT-Base on various downstream tasks including Semantic Similarity, Natural Language Inference and Sentiment Analysis. </p> </div> </div> </div> </div> </div> </div>

scholarly journals Information Content-Based Gene Ontology Semantic Similarity Approaches: Toward a Unified Framework Theory

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Gaston K. Mazandu ◽  
Nicola J. Mulder
Keyword(s):  
Gene Ontology ◽  
Information Content ◽  
Semantic Similarity ◽  
Experimental Evaluation ◽  
Similarity Measures ◽  
Mathematical Framework ◽  
Unified Framework ◽  
The Impact ◽  
Unified Description ◽  
Similarity Scores

Several approaches have been proposed for computing term information content (IC) and semantic similarity scores within the gene ontology (GO) directed acyclic graph (DAG). These approaches contributed to improving protein analyses at the functional level. Considering the recent proliferation of these approaches, a unified theory in a well-defined mathematical framework is necessary in order to provide a theoretical basis for validating these approaches. We review the existing IC-based ontological similarity approaches developed in the context of biomedical and bioinformatics fields to propose a general framework and unified description of all these measures. We have conducted an experimental evaluation to assess the impact of IC approaches, different normalization models, and correction factors on the performance of a functional similarity metric. Results reveal that considering only parents or only children of terms when assessing information content or semantic similarity scores negatively impacts the approach under consideration. This study produces a unified framework for current and future GO semantic similarity measures and provides theoretical basics for comparing different approaches. The experimental evaluation of different approaches based on different term information content models paves the way towards a solution to the issue of scoring a term’s specificity in the GO DAG.

scholarly journals Refining Word Representations by Manifold Learning

2019 ◽  
Author(s):  
Chu Yonghe ◽  
Hongfei Lin ◽  
Liang Yang ◽  
Yufeng Diao ◽  
Shaowu Zhang ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Euclidean Space ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Manifold Learning ◽  
Geometric Structure ◽  
Semantic Relatedness ◽  
Experimental Results ◽  
Proposed Model

Pre-trained distributed word representations have been proven useful in various natural language processing (NLP) tasks. However, the effect of words’ geometric structure on word representations has not been carefully studied yet. The existing word representations methods underestimate the words whose distances are close in the Euclidean space, while overestimating words with a much greater distance. In this paper, we propose a word vector refinement model to correct the pre-trained word embedding, which brings the similarity of words in Euclidean space closer to word semantics by using manifold learning. This approach is theoretically founded in the metric recovery paradigm. Our word representations have been evaluated on a variety of lexical-level intrinsic tasks (semantic relatedness, semantic similarity) and the experimental results show that the proposed model outperforms several popular word representations approaches.

scholarly journals DIBERT: Dependency Injected Bidirectional Encoder Representations from Transformers

2021 ◽  
Author(s):  
Abdul Wahab ◽  
Rafet Sifa
Keyword(s):  
Natural Language ◽  
Sentiment Analysis ◽  
Semantic Similarity ◽  
Syntactic Structure ◽  
Language Modeling ◽  
Benchmark Dataset ◽  
Fine Tuning ◽  
New Model ◽  
Dependency Tree ◽  
Better Than

<div> <div> <div> <p> </p><div> <div> <div> <p>In this paper, we propose a new model named DIBERT which stands for Dependency Injected Bidirectional Encoder Representations from Transformers. DIBERT is a variation of the BERT and has an additional third objective called Parent Prediction (PP) apart from Masked Language Modeling (MLM) and Next Sentence Prediction (NSP). PP injects the syntactic structure of a dependency tree while pre-training the DIBERT which generates syntax-aware generic representations. We use the WikiText-103 benchmark dataset to pre-train both BERT- Base and DIBERT. After fine-tuning, we observe that DIBERT performs better than BERT-Base on various downstream tasks including Semantic Similarity, Natural Language Inference and Sentiment Analysis. </p> </div> </div> </div> </div> </div> </div>

THE SOURCES OF AUXIN HORMONE TO GROWTH OF SHOOT PINEAPPLE STEM CUTTING (Ananascomosus.L. Merr.)

2016 ◽  
Vol 1 (1) ◽  
pp. 45-52
Author(s):  
Palupi Puspitorini
Keyword(s):  
Water Concentration ◽  
Urine Concentration ◽  
Experimental Results ◽  
Stem Cuttings ◽  
Randomized Design ◽  
Natural Hormone ◽  
Cow Urine ◽  
Urine Concentrations ◽  
Completely Randomized Design ◽  
Better Than

The aim of this study was to select the best sources of auxin of which it can stimulate the growth of shoots Pineapple plant cuttings. This research is compiled in a completely randomized design (CRD) with 4 treatments and 6 replications. The Data were statistically Analyzed by the DMRT. Level of treatment given proves that no treatment 0%, cow urine concentration of 25%, young coconut water concentration of 25% and Rootone F 100 mg / cuttings. The results showed that cow urine concentrations of 25% and Rootone F 100 mg give the best results in stimulating the growth of shoots pineapple stem cuttings. Experimental results concluded that the effect of this natural hormone were better than the shoots without given hormone.           

A Computational Method for the Identification of Endolysins and Autolysins

2020 ◽  
Vol 27 (4) ◽  
pp. 329-336 ◽  
Author(s):  
Lei Xu ◽  
Guangmin Liang ◽  
Baowen Chen ◽  
Xu Tan ◽  
Huaikun Xiang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Cell Wall ◽  
Experimental Results ◽  
Computational Method ◽  
Lytic Enzyme ◽  
Support Vector ◽  
Lytic Enzymes ◽  
Data Set ◽  
Optimal Feature ◽  
Better Than

Background: Cell lytic enzyme is a kind of highly evolved protein, which can destroy the cell structure and kill the bacteria. Compared with antibiotics, cell lytic enzyme will not cause serious problem of drug resistance of pathogenic bacteria. Thus, the study of cell wall lytic enzymes aims at finding an efficient way for curing bacteria infectious. Compared with using antibiotics, the problem of drug resistance becomes more serious. Therefore, it is a good choice for curing bacterial infections by using cell lytic enzymes. Cell lytic enzyme includes endolysin and autolysin and the difference between them is the purpose of the break of cell wall. The identification of the type of cell lytic enzymes is meaningful for the study of cell wall enzymes. Objective: In this article, our motivation is to predict the type of cell lytic enzyme. Cell lytic enzyme is helpful for killing bacteria, so it is meaningful for study the type of cell lytic enzyme. However, it is time consuming to detect the type of cell lytic enzyme by experimental methods. Thus, an efficient computational method for the type of cell lytic enzyme prediction is proposed in our work. Method: We propose a computational method for the prediction of endolysin and autolysin. First, a data set containing 27 endolysins and 41 autolysins is built. Then the protein is represented by tripeptides composition. The features are selected with larger confidence degree. At last, the classifier is trained by the labeled vectors based on support vector machine. The learned classifier is used to predict the type of cell lytic enzyme. Results: Following the proposed method, the experimental results show that the overall accuracy can attain 97.06%, when 44 features are selected. Compared with Ding's method, our method improves the overall accuracy by nearly 4.5% ((97.06-92.9)/92.9%). The performance of our proposed method is stable, when the selected feature number is from 40 to 70. The overall accuracy of tripeptides optimal feature set is 94.12%, and the overall accuracy of Chou's amphiphilic PseAAC method is 76.2%. The experimental results also demonstrate that the overall accuracy is improved by nearly 18% when using the tripeptides optimal feature set. Conclusion: The paper proposed an efficient method for identifying endolysin and autolysin. In this paper, support vector machine is used to predict the type of cell lytic enzyme. The experimental results show that the overall accuracy of the proposed method is 94.12%, which is better than some existing methods. In conclusion, the selected 44 features can improve the overall accuracy for identification of the type of cell lytic enzyme. Support vector machine performs better than other classifiers when using the selected feature set on the benchmark data set.

scholarly journals A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity

AERA Open ◽  
2021 ◽  
Vol 7 ◽  
pp. 233285842110286
Author(s):  
Kylie L. Anglin ◽  
Vivian C. Wong ◽  
Arielle Boguslav
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Intervention Implementation ◽  
Proof Of Concept ◽  
Coaching Intervention ◽  
Processing Techniques ◽  
Teacher Coaching ◽  
The Impact

Though there is widespread recognition of the importance of implementation research, evaluators often face intense logistical, budgetary, and methodological challenges in their efforts to assess intervention implementation in the field. This article proposes a set of natural language processing techniques called semantic similarity as an innovative and scalable method of measuring implementation constructs. Semantic similarity methods are an automated approach to quantifying the similarity between texts. By applying semantic similarity to transcripts of intervention sessions, researchers can use the method to determine whether an intervention was delivered with adherence to a structured protocol, and the extent to which an intervention was replicated with consistency across sessions, sites, and studies. This article provides an overview of semantic similarity methods, describes their application within the context of educational evaluations, and provides a proof of concept using an experimental study of the impact of a standardized teacher coaching intervention.

scholarly journals Cache-efficient sweeping-based interval joins for extended Allen relation predicates

2021 ◽  
Author(s):  
Danila Piatov ◽  
Sven Helmer ◽  
Anton Dignös ◽  
Fabio Persia
Keyword(s):  
Data Structure ◽  
Experimental Evaluation ◽  
State Of The Art ◽  
Temporal Databases ◽  
Access Method ◽  
Wide Range ◽  
Interval Relation ◽  
Cache Efficient ◽  
Join Algorithms ◽  
Better Than

AbstractWe develop a family of efficient plane-sweeping interval join algorithms for evaluating a wide range of interval predicates such as Allen’s relationships and parameterized relationships. Our technique is based on a framework, components of which can be flexibly combined in different manners to support the required interval relation. In temporal databases, our algorithms can exploit a well-known and flexible access method, the Timeline Index, thus expanding the set of operations it supports even further. Additionally, employing a compact data structure, the gapless hash map, we utilize the CPU cache efficiently. In an experimental evaluation, we show that our approach is several times faster and scales better than state-of-the-art techniques, while being much better suited for real-time event processing.

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