Embedding-based Detection and Extraction of Research Topics from Academic Documents Using Deep Clustering Cover

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Embedding-based Detection and Extraction of Research Topics from Academic Documents Using Deep Clustering

Journal of Data and Information Science

Volume 6 (2021): Issue 3 (June 2021)

By: Sahand Vahidnia, Alireza Abbasi and Hussein A. Abbass

Open Access

|Jun 2021

Figures & Tables

The overall and simplified process and objectives.

PV-DM model diagram.

DEC Autoencoder diagram. K is the number of clusters.

Additional WoS data figure.

Data growth from 1970 to 2019 in the three journals, yielding over about 4300 records.

Diagram of clusters in the study period. Y axis is the number of articles (symmetrical). X axis is years.

N-gram dictionary histogram of the dataset. Y-axis is frequency and X-axis is N.

Comparison of clustering techniques for pre-trained BERT embeddings_

Embedding	Clustering	Evaluation Metrics

		NMI	AMI	ARI
BERT (uncased A12)	kmeans (rand)	0.19507	0.1948	0.1442
	kmeans++	0.1950	0.1948	0.1442
	Agglomerative	0.2158	0.2156	0.1877
	DBSCAN	0.0042	0.0037	0.0001
	DEC	0.2568	0.2566	0.2377
SciBERT	kmeans (rand)	0.1498	0.1496	0.1266
	kmeans++	0.1492	0.1489	0.1266
	Agglomerative	0.1903	0.1901	0.1505
	DBSCAN	0.0042	0.0037	0.0001
	DEC	0.1776	0.1774	0.1731

Ordered key phrases of the clusters_

Cluster #	Terms (Normalized TF-IDF score)
c 1	creativity(1.00), sentiment_analysis(0.85), university(0.81), facial(0.79), insect(0.74), dreyfus(0.71), expert_system(0.67), music(0.65), indian_language(0.64), recommendation(0.63), argumentation(0.62), swarm(0.62), data_mining(0.61), face_recognition(0.61), natural_language_processing(0.60)
c 2	ois(1.00), execution(0.98), sinix(0.88), perception(0.80), people(0.75), unix(0.69), team(0.66), discourse(0.62), intention(0.57
c 3	revision(1.00), contraction(0.70), postulate(0.65), horn(0.65)
c 4	csp(1.00), propagation(0.80), arc_consistency(0.75), backjumping(0.59)
c 5	description_logic(1.00), deep_learning(0.89), ontology(0.74), rcc(0.56)
c 6	auction(1.00), equilibrium(0.74), election(0.66), coalition(0.66), bargaining(0.56)
c 7	support_vector_machine(1.00), classifier(0.68), knee(0.66)
c 8	document(1.00), wikipedia(0.99), wordnet(0.68), dictionary(0.63)
c 9	phase_transition(1.00), minimax(0.89), voting(0.87), alpha_beta(0.75), chess(0.69), backbone(0.64), optimal_solution(0.63), heuristic_function(0.63), game_tree(0.61), ratio(0.59), heuristic_search(0.59), monte_carlo_tree_search(0.55)
c 10	execution(1.00), reward(0.80), ebl(0.77), pomdp(0.68), team(0.66), heuristic_search(0.64), action_model(0.63), portfolio(0.60), monte_carlo_tree_search(0.59), mdp(0.59), conformant(0.58), mdps(0.57)

Comparison of clustering techniques with various embeddings on two different training corpora_

Training Dataset =>		KIPRIS			WoS+KIPRIS

Evaluation metrics=>		NMI	AMI	ARI	NMI	AMI	ARI
Embedding	Clustering
6*FastText (mean)	K-means (rand)	0.379	0.379	0.312	0.387	0.387	0.327
	K-means (++)	0.379	0.379	0.322	0.387	0.387	0.327
	Hierarchy Aggl.	0.391	0.391	0.289	0.363	0.363	0.306
	DBSCAN	0.006	0.005	0.000	0.005	0.005	0.000
	DEC	0.511	0.511	0.504	0.459	0.459	0.400
	DEC (scaled)	0.329	0.329	0.268	0.284	0.283	0.239
6*FastText (w. mean)	K-means (rand)	0.243	0.243	0.186	0.239	0.239	0.184
	K-means (++)	0.243	0.243	0.186	0.239	0.239	0.184
	Hierarchy Aggl.	0.260	0.260	0.140	0.234	0.234	0.176
	DBSCAN	0.037	0.035	0.001	0.011	0.010	0.000
	DEC	0.348	0.347	0.321	0.352	0.352	0.300
	DEC (scaled)	0.201	0.201	0.169	0.172	0.172	0.158
6*Doc2Vec	K-means (rand)	0.586	0.586	0.629	0.712	0.712	0.742
	K-means (++)	0.586	0.586	0.630	0.711	0.711	0.741
	Hierarchy Aggl.	0.444	0.444	0.457	0.602	0.602	0.633
	DBSCAN	0.004	0.004	0.000	0.004	0.004	0.000
	DEC	0.600	0.600	0.629	0.734	0.734	0.759
	DEC (scaled)	0.235	0.235	0.220	0.322	0.322	0.279
Topic Modeling			NMI	AMI	ARI
LDA			0.350	0.350	0.291

DOI: https://doi.org/10.2478/jdis-2021-0024 | Journal eISSN: 2543-683X | Journal ISSN: 2096-157X

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Language: English

Page range: 99 - 122

Submitted on: Nov 30, 2020

Accepted on: Apr 26, 2021

Published on: Jun 18, 2021

Published by: Chinese Academy of Sciences, National Science Library

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

Dynamics of science,

Science mapping,

Document clustering,

Artificial intelligence,

Related subjects:

Computer sciences,

Information technology,

Project management,

Databases and data mining

© 2021 Sahand Vahidnia, Alireza Abbasi, Hussein A. Abbass, published by Chinese Academy of Sciences, National Science Library
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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