To bring bibliometrics and information retrieval closer together, we propose to add the concept of author attribution into the pre-processing of scientific publications. Presently, common bibliographic metrics often attribute the entire article to all the authors affecting author-specific retrieval processes. We envision a more finegrained analysis of scientific authorship by attributing particular segments to authors. To realize this vision, we propose a new feature representation of scientific publications that captures the distribution of stylometric features. In a classification setting, we then seek to predict the number of authors of a scientific article. We evaluate our approach on a data set of ~ 6100 PubMed articles and achieve best results by applying random forests, i.e., 0.76 precision and 0.76 recall averaged over all classes.
The ongoing growth of the volume of scholarly publications poses significant challenges to both information retrieval processes in digital libraries as well as bibliometric techniques that analyse academic literature in a quantitative manner. Ideas from other fields such as computer linguistics have been incorporated into bibliometrics to improve and enhance the measuring and analysis processes.
To bring bibliometrics and information retrieval closer together, we propose to add the concept of author attribution into the pre-processing of the analysis of scientific publications. Yet, since common bibliographic metrics often attribute the entire article to all the authors, we introduce a reinterpretation of authorship attribution: to attribute particular segments of an article to individual authors allowing for a more fine-grained analysis of contribution and role. Information retrieval systems could then benefit from such authorship attribution in the following ways: Scholarly search engines could implement an author specific search which allows researchers to specifically look for text passages written by a particular author. This more precise passage-author attribution then allows the generation of researcher profiles. These profiles would reflect a researcher’s contributions to different scientific fields in a more detailed manner. In addition, the profile might be valuable for predicting and thus understanding a researcher’s role, for example, more actively involved (writing) vs. acting more like a mentor providing ideas and giving feedback (less involved in writing; reflected for example by author positioning).
As a first step in this direction we have recently applied text segmentation to identify potential
author changes within the main text of a scientific article
The text for the analysis is produced by a PDF processing pipeline, which analyses scientific
articles and extracts, among other information, also the main text
Over the past decades one can observe an ever growing amount of scientific output; much to the joy of research areas such as (i) Bibliometrics which applies statistics to measure scientific impact and (ii) Information Retrieval which applies natural language processing to make the valuable body of knowledge accessible. This interest in processing and exploiting scientific publications from different perspectives is reflected by venues such as the International Workshop on Bibliometric-enhanced Information Retrieval (cf. (Mayr et al.)), the International Workshop on Mining Scientific Publications1 or Mining Scientific Papers: Computational Linguistics and Bibliometrics2.
To be of value for both fields, scientific publications need to be semantically enriched. Adding
semantics includes assigning instances to concepts which are organized and structured in
dedicated ontologies. Entity and relation recognition thus represent a vital pre-processing
step. To give an example, medical entity recognition (cf.
Departing from a mere content-level, Liakata et al. (cf.
In this paper we introduce the concept of authorship attribution as an additional
preprocessing step for subsequent retrieval procedures. Authorship attribution, in general,
expresses a classification setting where from a set of candidate authors the author of a
questioned article is to be selected (cf.
For the evaluation we use a dataset composed of randomly selected documents from PubMed (http://www.ncbi.nlm.nih.gov/pubmed/), a free database created by the US National Library of Medicine holding full-text articles from the biomedical domain together with a standard XML mark-up that rigorously annotates the complete content of the published document, in particular the author metadata. The documents contained in this database are very diverse. In this work we limit ourselves to research articles only, but there is also a wide range of different article types, including book reviews and meeting reports.
For this evaluation we selected a subset of the PubMed dataset consisting of an approximately equal number of research articles written by a certain number of authors, ranging from one to five. For our evaluation, we chose 6144 research articles in total, across 563 different journals and publication entities. There were 983, 1192, 1391, 1418, and 1160 articles with one, two, three, four, and five authors, respectively.
A prerequisite for the writing style analysis of scientific articles is the reliable extraction of
their textual content. The portable document format (PDF), the most common format for
scientific literature today, is optimised for presentation, but lacks structural information. As
the raw character stream of the PDF is usually interrupted in mid-sentence by decorations or
floating objects, extracting the main text of a scholarly article in the correct order requires the
analysis of its document structure. To solve this task we build here upon our previous work
The first step builds upon the output of the Apache PDFBox library (http://pdfbox.apache.org)
and uses unsupervised learning (clustering) to extract blocks of contiguous text from the raw
PDF file and their column-wise reading order on each page. We consider these text blocks as
the basic building blocks of a scientific article. In the next stage, these text blocks are
categorized into different logical labels based on their role within the document: meta-data
blocks, decorations, figure and table captions, main text, and section headings. This stage is
implemented as a sequential pipeline of detectors each of which labels a specific type of
block. Apart from the meta-data detectors they are completely model-free and unsupervised.
For more details on each of these detectors the interested reader is referred to
Capturing different writing styles within a document requires the extraction and analysis of suitable features. Topical features, such as word unigrams or other elements carrying semantic information, are helpful in identifying document segments which differ not only in the author, but also in the whole topic of the text. On the other hand, stylometric features reflect the author’s writing style, rather than the topic, which typically does not change within a single scientific article, and generalizes across different domains.
To compare and classify different scientific articles based on the number of authors involved, we try to capture the distribution of stylometric features across a single document. We split the document into continuous segments (here a segment corresponds to a sentence) and extract the stylometric features for each of those segments. We then view the document as a distribution of different stylometric features.
The literature suggests a broad amount of stylometric features
In order to evaluate whether the stylometric feature representation of scientific articles contains authorship information, we trained different classifiers in a supervised manner to predict the number of authors for each document. From the articles in the PubMed dataset, we extracted the stylometric features for each sentence of the document and represented the distribution of this features across the document as its maximum, minimum, average and variance. As a further preprocessing step we normalized these feature values to avoid dominating features in the learning process. For our experiments, we selected two classification algorithms: Logistic regression and Random Forest. correctness of the data. The 1-author papers are less likely to have more authors than mentioned in the paper, making the data more representative for this class.
These experiments demonstrate that in the proposed stylometric feature space it is possible to a certain extent to discriminate between scientific articles with different numbers of authors.
In this paper, we classified scientific articles according to their number of authors by using a set of stylometric features. We applied supervised learning to this setup and achieved best results with Random Forests. The classification results suggest that the stylometric feature space in fact captures variations in the writing style that we would expect from multiple contributing authors.
This work fosters our understanding towards a more fine-grained analysis of scientific authorship by attributing particular segments to authors. Information retrieval systems could benefit from this concept of authorship attribution, for instance, in course of author specific search.
This work is funded by the KIRAS program of the Austrian Research Promotion Agency (FFG) (project number 840824). The Know-Center is funded within the Austrian COMET Program under the auspices of the Austrian Ministry of Transport, Innovation and Technology, the Austrian Ministry of Economics and Labour and by the State of Styria. COMET is managed by the Austrian Research Promotion Agency FFG.