20 research outputs found
C-Rex: A Comprehensive System for Recommending In-Text Citations with Explanations
Finding suitable citations for scientific publications can be challenging and time-consuming. To this end, context-aware citation recommendation approaches that recommend publications as candidates for in-text citations have been developed. In this paper, we present C-Rex, a web-based demonstration system available at http://c-rex.org for context-aware citation recommendation based on the Neural Citation Network [5] and millions of publications from the Microsoft Academic Graph. Our system is one of the first online context-aware citation recommendation systems and the first to incorporate not only a deep learning recommendation approach, but also explanation components to help users better understand why papers were recommended. In our offline evaluation, our model performs similarly to the one presented in the original paper and can serve as a basic framework for further implementations. In our online evaluation, we found that the explanations of recommendations increased users’ satisfaction
Will This Paper Increase Your h-index? Scientific Impact Prediction
Scientific impact plays a central role in the evaluation of the output of
scholars, departments, and institutions. A widely used measure of scientific
impact is citations, with a growing body of literature focused on predicting
the number of citations obtained by any given publication. The effectiveness of
such predictions, however, is fundamentally limited by the power-law
distribution of citations, whereby publications with few citations are
extremely common and publications with many citations are relatively rare.
Given this limitation, in this work we instead address a related question asked
by many academic researchers in the course of writing a paper, namely: "Will
this paper increase my h-index?" Using a real academic dataset with over 1.7
million authors, 2 million papers, and 8 million citation relationships from
the premier online academic service ArnetMiner, we formalize a novel scientific
impact prediction problem to examine several factors that can drive a paper to
increase the primary author's h-index. We find that the researcher's authority
on the publication topic and the venue in which the paper is published are
crucial factors to the increase of the primary author's h-index, while the
topic popularity and the co-authors' h-indices are of surprisingly little
relevance. By leveraging relevant factors, we find a greater than 87.5%
potential predictability for whether a paper will contribute to an author's
h-index within five years. As a further experiment, we generate a
self-prediction for this paper, estimating that there is a 76% probability that
it will contribute to the h-index of the co-author with the highest current
h-index in five years. We conclude that our findings on the quantification of
scientific impact can help researchers to expand their influence and more
effectively leverage their position of "standing on the shoulders of giants."Comment: Proc. of the 8th ACM International Conference on Web Search and Data
Mining (WSDM'15
Towards a Modular Recommender System for Research Papers written in Albanian
In the recent years there has been an increase in scientific papers
publications in Albania and its neighboring countries that have large
communities of Albanian speaking researchers. Many of these papers are written
in Albanian. It is a very time consuming task to find papers related to the
researchers' work, because there is no concrete system that facilitates this
process. In this paper we present the design of a modular intelligent search
system for articles written in Albanian. The main part of it is the recommender
module that facilitates searching by providing relevant articles to the users
(in comparison with a given one). We used a cosine similarity based heuristics
that differentiates the importance of term frequencies based on their location
in the article. We did not notice big differences on the recommendation results
when using different combinations of the importance factors of the keywords,
title, abstract and body. We got similar results when using only the title and
abstract in comparison with the other combinations. Because we got fairly good
results in this initial approach, we believe that similar recommender systems
for documents written in Albanian can be build also in contexts not related to
scientific publishing.Comment: 8 page
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Automating Citation Placement with Natural Language Processing and Transformers
In scientific writing, references are crucial in supporting claims, spotlighting evidence, and highlighting research gaps. However, where to add a reference and which reference to cite are subjectively chosen by the papers’ authors; thus the automation of the task is challenging and requires proper investigations. This paper focuses on the automatic placement of references, considering its diverse approaches depending on writing style and community norms, and investigates the use of transformers and Natural Language Processing heuristics to predict i) if a reference is needed in a scientific statement, and ii) where the reference should be placed within the statement. For this investigation, this paper investigates two techniques, namely Mask-filling (MF) and Named Entity Recognition (NER), and provides insights on how to solve this task