About: Strong Baselines for Author Name Disambiguation with and Without Neural Networks

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Strong Baselines for Author Name Disambiguation with and Without Neural Networks Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Strong Baselines for Author Name Disambiguation with and Without Neural Networks
Creator	Wang, Dong Zhang, Zhenyu Liu, Tingwen Yu, Bowen
Source	PMC
abstract	Author name disambiguation (AND) is one of the most vital problems in scientometrics, which has become a great challenge with the rapid growth of academic digital libraries. Existing approaches for this task substantially rely on complex clustering-like architectures, and they usually assume the number of clusters is known beforehand or predict the number by applying another model, which involve increasingly complex and time-consuming architectures. In this paper, we combine simple neural networks with two sets of heuristic rules to explore strong baselines for the author name disambiguation problem without any priori knowledge or estimation about cluster size, which frees the model from unnecessary complexity. On a popular benchmark dataset AMiner, our solution significantly outperforms several state-of-the-art methods both in performance and efficiency, and it still achieves comparable performance with many complex models when only using a group of rules. Experimental results also indicate that gains from sophisticated deep learning techniques are quite modest in the author name disambiguation problem.
has issue date	2020-04-17(xsd:dateTime)
bibo:doi	10.1007/978-3-030-47426-3_29
has license	no-cc
sha1sum (hex)	b2863fd4ed875e287d64390159dce946207dd849
schema:url	https://doi.org/10.1007/978-3-030-47426-3_29
resource representing a document's title	Strong Baselines for Author Name Disambiguation with and Without Neural Networks
has PubMed Central identifier	PMC7206288
schema:publication	Advances in Knowledge Discovery and Data Mining
resource representing a document's body	covid:b2863fd4ed875e287d64390159dce946207dd849#body_text
is schema:about of	named entity 'model' named entity 'great' named entity 'vital' named entity 'AND' named entity 'NEURAL NETWORKS' named entity 'USUALLY' named entity 'PROBLEM' named entity 'EFFICIENCY' named entity 'MODELS' named entity 'GROWTH' named entity 'KNOWN' named entity 'ESTIMATION' named entity 'STATE' named entity 'RAPID' named entity 'SOLUTION' named entity 'NUMBER' named entity 'COMBINE' named entity 'complex' named entity 'sophisticated' named entity 'predict' named entity 'simple' named entity 'scientometrics' named entity 'neural networks' named entity 'number of clusters' named entity 'deep learning' named entity 'AMiner' named entity 'combine' named entity 'AMiner' named entity 'metadata' named entity 'ACM Digital Library' named entity 'HAC' named entity 'upper bound' named entity 'word embeddings' named entity 'auto-encoder' named entity 'cluster publications' named entity 'training set' named entity 'AMiner' named entity 'weight decay' named entity 'neural networks' named entity 'semantic' named entity 'graph embedding' named entity 'algorithm' named entity 'neural networks' named entity 'information retrieval' named entity '0.9' named entity 'neural networks' named entity 'error propagation' named entity 'semi-supervised' named entity 'HAC' named entity 'heuristic rules' named entity 'art model' named entity 'word embeddings' named entity 'feature engineering' named entity 'CNN' named entity 'AMiner' named entity 'heuristic rules' named entity 'connected components' named entity 'computationally expensive' named entity '95%' named entity 'semantic similarity' named entity 'probabilistic' named entity 'cross validation' named entity 'Stochastic Gradient Descent' named entity 'Basic Rules' named entity 'denoising' named entity 'learning rate' named entity 'AMiner' named entity 'distance function' named entity 'hierarchical clustering' named entity 'Basic Rules' named entity 'representation learning' named entity 'Markov Random Fields' named entity 'Scopus' named entity 'Graph-based methods' named entity 'convolutional neural networks' named entity 'natural law' named entity 'DBSCAN' named entity 'main point' named entity 'data analysis' named entity 'Precision, Recall'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software