scholarly journals Evaluating semantometrics from computer science publications

2020 ◽  
Vol 125 (3) ◽  
pp. 2915-2954
Author(s):  
Christin Katharina Kreutz ◽  
Premtim Sahitaj ◽  
Ralf Schenkel
Keyword(s):  
State Of The Art ◽  
Binary Classification ◽  
Citation Network ◽  
Distance Measures ◽  
Semantic Features ◽  
Citation Networks ◽  
Current State ◽  
Publication Time ◽  
Citation Practices ◽  
Vector Representations

AbstractIdentification of important works and assessment of importance of publications in vast scientific corpora are challenging yet common tasks subjected by many research projects. While the influence of citations in finding seminal papers has been analysed thoroughly, citation-based approaches come with several problems. Their impracticality when confronted with new publications which did not yet receive any citations, area-dependent citation practices and different reasons for citing are only a few drawbacks of them. Methods relying on more than citations, for example semantic features such as words or topics contained in publications of citation networks, are regarded with less vigour while providing promising preliminary results. In this work we tackle the issue of classifying publications with their respective referenced and citing papers as either seminal, survey or uninfluential by utilising semantometrics. We use distance measures over words, semantics, topics and publication years of papers in their citation network to engineer features on which we predict the class of a publication. We present the SUSdblp dataset consisting of 1980 labelled entries to provide a means of evaluating this approach. A classification accuracy of up to .9247 was achieved when combining multiple types of features using semantometrics. This is +.1232 compared to the current state of the art (SOTA) which uses binary classification to identify papers from classes seminal and survey. The utilisation of one-vector representations for the ternary classification task resulted in an accuracy of .949 which is +.1475 compared to the binary SOTA. Classification based on information available at publication time derived with semantometrics resulted in an accuracy of .8152 while an accuracy of .9323 could be achieved when using one-vector representations.

scholarly journals A Novel Deep Neural Network-Based Approach to Measure Scholarly Research Dissemination Using Citations Network

2021 ◽  
Vol 11 (22) ◽  
pp. 10970
Author(s):  
Naif Radi Aljohani ◽  
Ayman Fayoumi ◽  
Saeed-Ul Hassan
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Binary Classification ◽  
Class Imbalance ◽  
Citation Network ◽  
Graph Visualization ◽  
Scholarly Research ◽  
Research Dissemination ◽  
Reference Corpus ◽  
Feature Based

We investigated the scientific research dissemination by analyzing the publications and citation data, implying that not all citations are significantly important. Therefore, as alluded to existing state-of-the-art models that employ feature-based techniques to measure the scholarly research dissemination between multiple entities, our model implements the convolutional neural network (CNN) with fastText-based pre-trained embedding vectors, utilizes only the citation context as its input to distinguish between important and non-important citations. Moreover, we speculate using focal-loss and class weight methods to address the inherited class imbalance problems in citation classification datasets. Using a dataset of 10 K annotated citation contexts, we achieved an accuracy of 90.7% along with a 90.6% f1-score, in the case of binary classification. Finally, we present a case study to measure the comprehensiveness of our deployed model on a dataset of 3100 K citations taken from the ACL Anthology Reference Corpus. We employed state-of-the-art graph visualization open-source tool Gephi to analyze the various aspects of citation network graphs, for each respective citation behavior.

scholarly journals Exploring Compositional Architectures and Word Vector Representations for Prepositional Phrase Attachment

2014 ◽  
Vol 2 ◽  
pp. 561-572 ◽  
Author(s):  
Yonatan Belinkov ◽  
Tao Lei ◽  
Regina Barzilay ◽  
Amir Globerson
Keyword(s):  
State Of The Art ◽  
Prepositional Phrase ◽  
Unlabeled Data ◽  
Svm Classifier ◽  
Semantic Features ◽  
Syntactic Parsing ◽  
Non Linear ◽  
Promising Solution ◽  
Vector Representations ◽  
Performance Gains

Prepositional phrase (PP) attachment disambiguation is a known challenge in syntactic parsing. The lexical sparsity associated with PP attachments motivates research in word representations that can capture pertinent syntactic and semantic features of the word. One promising solution is to use word vectors induced from large amounts of raw text. However, state-of-the-art systems that employ such representations yield modest gains in PP attachment accuracy. In this paper, we show that word vector representations can yield significant PP attachment performance gains. This is achieved via a non-linear architecture that is discriminatively trained to maximize PP attachment accuracy. The architecture is initialized with word vectors trained from unlabeled data, and relearns those to maximize attachment accuracy. We obtain additional performance gains with alternative representations such as dependency-based word vectors. When tested on both English and Arabic datasets, our method outperforms both a strong SVM classifier and state-of-the-art parsers. For instance, we achieve 82.6% PP attachment accuracy on Arabic, while the Turbo and Charniak self-trained parsers obtain 76.7% and 80.8% respectively.

A novel molecular representation with BiGRU neural networks for learning atom

2019 ◽  
Vol 21 (6) ◽  
pp. 2099-2111 ◽  
Author(s):  
Xuan Lin ◽  
Zhe Quan ◽  
Zhi-Jie Wang ◽  
Huang Huang ◽  
Xiangxiang Zeng
Keyword(s):  
Neural Networks ◽  
Drug Discovery ◽  
Related Problem ◽  
State Of The Art ◽  
Binary Classification ◽  
Classification Problems ◽  
Novel Method ◽  
Low Dimensional ◽  
Vector Representations ◽  
Gated Recurrent Unit

Abstract Molecular representations play critical roles in researching drug design and properties, and effective methods are beneficial to assisting in the calculation of molecules and solving related problem in drug discovery. In previous years, most of the traditional molecular representations are based on hand-crafted features and rely heavily on biological experimentations, which are often costly and time consuming. However, recent researches achieve promising results using machine learning on various domains. In this article, we present a novel method named Smi2Vec-BiGRU that is designed for learning atoms and solving the single- and multitask binary classification problems in the field of drug discovery, which are the basic and also key problems in this field. Specifically, our approach transforms the molecule data in the SMILES format into a set of sample vectors and then feeds them into the bidirectional gated recurrent unit neural networks for training, which learns low-dimensional vector representations for molecular drug. We conduct extensive experiments on several widely used benchmarks including Tox21, SIDER and ClinTox. The experimental results show that our approach can achieve state-of-the-art performance on these benchmarking datasets, demonstrating the feasibility and competitiveness of our proposed approach.

scholarly journals Syntax-Informed Self-Attention Network for Span-Based Joint Entity and Relation Extraction

2021 ◽  
Vol 11 (4) ◽  
pp. 1480
Author(s):  
Haiyang Zhang ◽  
Guanqun Zhang ◽  
Ricardo Ma
Keyword(s):  
State Of The Art ◽  
Relation Extraction ◽  
Local Context ◽  
Semantic Features ◽  
Global Context ◽  
Contextual Features ◽  
Current State ◽  
Syntactic Features ◽  
Dependency Trees ◽  
Depth Learning

Current state-of-the-art joint entity and relation extraction framework is based on span-level entity classification and relation identification between pairs of entity mentions. However, while maintaining an efficient exhaustive search on spans, the importance of syntactic features is not taken into consideration. It will lead to a problem that the prediction of a relation between two entities is related based on corresponding entity types, but in fact they are not related in the sentence. In addition, although previous works have proven that extract local context is beneficial for the task, it still lacks in-depth learning of contextual features in local context. In this paper, we propose to incorporate syntax knowledge into multi-head self-attention by employing part of heads to focus on syntactic parents of each token from pruned dependency trees, and we use it to model the global context to fuse syntactic and semantic features. In addition, in order to get richer contextual features from the local context, we apply local focus mechanism on entity pairs and corresponding context. Based on applying the two strategies, we perform joint entity and relation extraction on span-level. Experimental results show that our model achieves significant improvements on both Conll04 and SciERC dataset compared to strong competitors.

Behavioral Genetics: Concepts for Research and Practice in Language Development and Disorders

1995 ◽  
Vol 38 (5) ◽  
pp. 1126-1142 ◽  
Author(s):  
Jeffrey W. Gilger
Keyword(s):  
Language Development ◽  
Behavioral Genetics ◽  
State Of The Art ◽  
Genetic Research ◽  
Great Promise ◽  
Behavioral Genetic ◽  
Fine Grained ◽  
Future Goals ◽  
Current State ◽  
Research Designs

This paper is an introduction to behavioral genetics for researchers and practioners in language development and disorders. The specific aims are to illustrate some essential concepts and to show how behavioral genetic research can be applied to the language sciences. Past genetic research on language-related traits has tended to focus on simple etiology (i.e., the heritability or familiality of language skills). The current state of the art, however, suggests that great promise lies in addressing more complex questions through behavioral genetic paradigms. In terms of future goals it is suggested that: (a) more behavioral genetic work of all types should be done—including replications and expansions of preliminary studies already in print; (b) work should focus on fine-grained, theory-based phenotypes with research designs that can address complex questions in language development; and (c) work in this area should utilize a variety of samples and methods (e.g., twin and family samples, heritability and segregation analyses, linkage and association tests, etc.).

Schizophrenia Research: Current State of the Art

1976 ◽  
Vol 21 (7) ◽  
pp. 497-498
Author(s):  
STANLEY GRAND
Keyword(s):  
State Of The Art ◽  
Current State

Umbral Calculus

10.37236/24 ◽  
2002 ◽  
Vol 1000 ◽  
Author(s):  
A. Di Bucchianico ◽  
D. Loeb
Keyword(s):  
19Th Century ◽  
Finite Differences ◽  
State Of The Art ◽  
Umbral Calculus ◽  
Current State ◽  
Logical Foundation ◽  
Complete Bibliography ◽  
The 19Th Century ◽  
Mathematical Literature

We survey the mathematical literature on umbral calculus (otherwise known as the calculus of finite differences) from its roots in the 19th century (and earlier) as a set of “magic rules” for lowering and raising indices, through its rebirth in the 1970’s as Rota’s school set it on a firm logical foundation using operator methods, to the current state of the art with numerous generalizations and applications. The survey itself is complemented by a fairly complete bibliography (over 500 references) which we expect to update regularly.

The Receptor-Dependent QSAR Paradigm: An Overview of the Current State of the Art

2009 ◽  
Vol 5 (4) ◽  
pp. 359-366 ◽  
Author(s):  
Osvaldo Santos-Filho ◽  
Anton Hopfinger ◽  
Artem Cherkasov ◽  
Ricardo de Alencastro
Keyword(s):  
State Of The Art ◽  
Current State
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