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Deep-CENIC

This repository contains data and code of Deep-CENIC, a deep learning classifier for classifying ideational impact of research papers. In our Decision Support Systems paper: "Classifying the ideational impact of Information Systems review articles: A content-enriched deep learning approach", which provides further details on the data and model, Deep-CENIC was evaluated in the context of information systems (IS) review articles (RAs).

Goals

The repository was developed with reproducibility in mind, offering a starting point to reuse the following components:

  1. Definitions and implementation of NLP features (see table in the following Data section)
  2. Coding of ideational impact as a gold-standard for training and evaluating ML algorithms
  3. Blueprint for a citation analysis repository structure (based on Cookiecutter Data Science)

Data

The dataset contains IS RAs published in major IS journals (Lowry et al. 2013) between 2000 and 2014. A forward search has been conducted on a sample of the original dataset comprising only citing publications, which cite a RA from the IS business value domain and appeared in a journal included in the Senior Scholars' basket of journals. The citation data was extracted from Google Scholar and Web of Science. A large scale coding of the ideational impact of IS RAs was conducted, which serves as the input for the Deep-CENIC model. The data is structured into three stages:

  1. raw
  2. interim
  3. processed

Due to copyright restrictions some data has been removed from the repository or is presented in truncated form:

  • Raw TEI files: TEI files contain the full text of published papers and have therefore been removed. Refer to GROBID for details on how to generate TEI files from PDF papers.
  • raw/LR.csv, interim/LR.csv and interim/CP.csv contain paper abstracts which have been truncated.
  • interim/CITATION.csv contain citation sentences which have been truncated and only a random sample has been retained in the repository.

The final dataset (FEATURE_FRAME.csv) is fully available:

Feature Description Data Type Source
citation_key_lr Bibtex citation key String raw
citation_key_cp Bibtex citation key String raw
focal_citations Number of citations toward the focal RA Integer summarize_citation_df
textual Number of textual citations Integer is_textual_citation
separate Number of standalone citations Integer is_separate
comp_sup Number of comparative and superlative clauses Integer has_comp_sup
prp Number of personal pronouns Integer has_1st_3rd_prp
pos_0 Appearances of POS pattern 0 Integer find_pos_patterns
pos_1 Appearances of POS pattern 1 Integer find_pos_patterns
pos_2 Appearances of POS pattern 2 Integer find_pos_patterns
pos_3 Appearances of POS pattern 3 Integer find_pos_patterns
pos_4 Appearances of POS pattern 4 Integer find_pos_patterns
pos_5 Appearances of POS pattern 5 Integer find_pos_patterns
sentence_popularity amin Number of different citations in the citing sentence (min aggregate) Integer get_popularity
sentence_popularity amax Number of different citations in the citing sentence (max aggregate) Integer get_popularity
sentence_popularity mean Number of different citations in the citing sentence (mean aggregate) Double get_popularity
context_popularity amin Number of different citations in the citing context (min aggregate) Integer get_popularity
context_popularity amax Number of different citations in the citing context (max aggregate) Integer get_popularity
context_popularity mean Number of different citations in the citing context (mean aggregate) Double get_popularity
sentence_density amin Focal citations divided by total citations in the citing sentence (min aggregate) Double get_density
sentence_density amax Focal citations divided by total citations in the citing sentence (max aggregate) Double get_density
sentence_density mean Focal citations divided by total citations in the citing sentence (mean aggregate) Double get_density
context_density amin Focal citations divided by total citations in the citing context (min aggregate) Double get_density
context_density amax Focal citations divided by total citations in the citing context (max aggregate) Double get_density
context_density mean Focal citations divided by total citations in the citing context (mean aggregate) Double get_density
position_in_sentence amin Position of the reference in citing sentence (min aggregate) Double get_position_in_sentence
position_in_sentence amax Position of the reference in citing sentence (max aggregate) Double get_position_in_sentence
position_in_sentence mean Position of the reference in citing sentence (mean aggregate) Double get_position_in_sentence
sentence_neg amin Negative sentiment of the citing sentence with regards to the RA (min aggregate) Double get_sentiment
sentence_neg amax Negative sentiment of the citing sentence with regards to the RA (max aggregate) Double get_sentiment
sentence_neg mean Negative sentiment of the citing sentence with regards to the RA (mean aggregate) Double get_sentiment
sentence_neu amin Neutral sentiment of the citing sentence with regards to the RA (min aggregate) Double get_sentiment
sentence_neu amax Neutral sentiment of the citing sentence with regards to the RA (max aggregate) Double get_sentiment
sentence_neu mean Neutral sentiment of the citing sentence with regards to the RA (mean aggregate) Double get_sentiment
sentence_pos amin Positive sentiment of the citing sentence with regards to the RA (min aggregate) Double get_sentiment
sentence_pos amax Positive sentiment of the citing sentence with regards to the RA (max aggregate) Double get_sentiment
sentence_pos mean Positive sentiment of the citing sentence with regards to the RA (mean aggregate) Double get_sentiment
sentence_compound amin Compound sentiment of the citing sentence with regards to the RA (min aggregate) Double get_sentiment
sentence_compound amax Compound sentiment of the citing sentence with regards to the RA (max aggregate) Double get_sentiment
sentence_compound mean Compound sentiment of the citing sentence with regards to the RA (mean aggregate) Double get_sentiment
context_neg amin Negative sentiment of the citing context with regards to the RA (min aggregate) Double get_sentiment
context_neg amax Negative sentiment of the citing context with regards to the RA (max aggregate) Double get_sentiment
context_neg mean Negative sentiment of the citing context with regards to the RA (mean aggregate) Double get_sentiment
context_neu amin Neutral sentiment of the citing context with regards to the RA (min aggregate) Double get_sentiment
context_neu amax Neutral sentiment of the citing context with regards to the RA (max aggregate) Double get_sentiment
context_neu mean Neutral sentiment of the citing context with regards to the RA (mean aggregate) Double get_sentiment
context_pos amin Positive sentiment of the citing context with regards to the RA (min aggregate) Double get_sentiment
context_pos amax Positive sentiment of the citing context with regards to the RA (max aggregate) Double get_sentiment
context_pos mean Positive sentiment of the citing context with regards to the RA (mean aggregate) Double get_sentiment
context_compound amin Compound sentiment of the citing context with regards to the RA (min aggregate) Double get_sentiment
context_compound amax Compound sentiment of the citing context with regards to the RA (max aggregate) Double get_sentiment
context_compound mean Compound sentiment of the citing context with regards to the RA (mean aggregate) Double get_sentiment
self_citation At least one author of the RA and CA is identical Boolean is_self_citation
title_similarity Semantic similarity of the RA and CA titles Double get_title_similarity
abstract_similarity Semantic similarity of the RA and CA abstracts Double get_abstract_similarity
SYN Knowledge developed in the cited RA includes synthesis Boolean raw
TT Knowledge developed in the cited RA includes theory testing Boolean raw
TB Knowledge developed in the cited RA includes theory building Boolean raw
RG Knowledge developed in the cited RA includes identification of research gaps Boolean raw
CRI Knowledge developed in the cited RA includes critical assessment Boolean raw
RA Knowledge developed in the cited RA includes development of a research agenda Boolean raw
total_references Total number of references in the CA Integer extract_total_references
total_citations Total number of citations in the CA Integer extract_total_citations
weighted_citation_count RA citations divided by total citations Double __main__
mention_positions_10 Number of citations in the first 10% of the paper Integer summarize_citation_df
mention_positions_20 Number of citations in the second 10% of the paper Integer summarize_citation_df
mention_positions_30 Number of citations in the third 10% of the paper Integer summarize_citation_df
mention_positions_40 Number of citations in the forth 10% of the paper Integer summarize_citation_df
mention_positions_50 Number of citations in the fifth 10% of the paper Integer summarize_citation_df
mention_positions_60 Number of citations in the sixth 10% of the paper Integer summarize_citation_df
mention_positions_70 Number of citations in the seventh 10% of the paper Integer summarize_citation_df
mention_positions_80 Number of citations in the eigth 10% of the paper Integer summarize_citation_df
mention_positions_90 Number of citations in the ninth 10% of the paper Integer summarize_citation_df
mention_positions_100 Number of citations in the tenth 10% of the paper Integer summarize_citation_df
heading_category_NA Number of citations in the rest of the paper Integer summarize_citation_df
heading_category_intro Number of citations in the introduction section Integer summarize_citation_df
heading_category_background Number of citations in the background section Integer summarize_citation_df
heading_category_theory Number of citations in the theory section Integer summarize_citation_df
heading_category_methods Number of citations in the methods section Integer summarize_citation_df
heading_category_results Number of citations in the results section Integer summarize_citation_df
heading_category_implications Number of citations in the implications section Integer summarize_citation_df
heading_category_appendix Number of citations in the appendix Integer summarize_citation_df
ref_in_title Citation in the title of the paper Boolean check_ref_in_title
ref_in_heading Number of citations in section headings Integer ref_in_heading
ref_in_figure_description Number of citations in figure captions Integer ref_in_figDesc
ref_in_table_description Number of citations in table captions Integer ref_in_tableDesc
USE Ideational impact target variable Boolean raw

Setup

  1. Download and install Docker
  2. Clone this repository: git clone https://github.com/julianprester/deep-cenic.git
  3. Build docker container: make dockerize
  4. Run code: make run

Model

The focus of this repository is on developing and providing an ideational impact dataset. Thus, it does not include the machine and deep learning models trained on the data. For details regarding the standard implementations using the Keras and Tensorflow libraries refer to the paper and the figure below.

Deep-CENIC model architecture

References

This repository is part of a broader research program, comprising the following work:

  • Schryen, G., Wagner, G., & Benlian, A. (2015). Theory of Knowledge for Literature Reviews: An Epistemological Model, Taxonomy and Empirical Analysis of IS Literature. In: Proceedings of the 36th International Conference on Information Systems, Fort Worth, Texas. link.
  • Wagner, G., Prester, J., Roche, M. P., Benlian, A., & Schryen, G. (2016). Factors Affecting the Scientific Impact of Literature Reviews: A Scientometric Study. In: Proceedings of the 37th International Conference on Information Systems, Dublin, Ireland. link.
  • Prester, J., & Wagner, G., & Schryen, G. (2018). Classifying the Ideational Impact of IS Review Articles: A Natural Language Processing Based Approach. In: Proceedings of the 39th International Conference on Information Systems, San Francisco, California. link.
  • Schryen, G., Wagner, G., Benlian, A., & Paré, G. (2020). A Knowledge Development Perspective on Literature Reviews: Validation of a new Typology in the IS Field. Communications of the Association for Information Systems, 46. link.
  • Schryen, G., Wagner, G., & Benlian, A. (2020). Distinguishing Knowledge Impact from Citation Impact: A Methodology for Analysing Knowledge Impact for the Literature Review Genre. Available at SSRN: link.
  • Hassan, N. R., Prester, J., & Wagner, G. (2020). Seeking Out Clear And Unique Information Systems Concepts: A Natural Language Processing Approach. In Proceedings of the 28th European Conference on Information Systems, Marrackech, Morocco. link.
  • Prester, J., & Wagner, G., Schryen, G., & Hassan, N. R. (2020). Classifying the Ideational Impact of Information Systems Review Articles: A Content-enriched Deep Learning Approach. Decision Support Systems, forthcoming. link.