Natural Language Processing for Intelligent Access to ScientificInformation
Horacio Saggion and Francesco Ronzano
DTICUniversitat Pompeu Fabra
Carrer Tanger 122, Barcelona (08018), Barcelona, Spain{name.surname}@upf.edu
Abstract
During the last decade the amount of scientific information available on-line increased at an unprecedented rate. As a consequence, nowadays researchers are overwhelmed by an enormousand continuously growing number of articles to consider when they perform research activities
like the exploration of advances in specific topics, peer reviewing, writing and evaluation ofproposals. Natural Language Processing Technology represents a key enabling factor in providing scientists with intelligent patterns to access to scientific information. Extracting informationfrom scientific papers, for example, can contribute to the development of rich scientific knowledge bases which can be leveraged to support intelligent knowledge access and question answering. Summarization techniques can reduce the size of long papers to their essential content or
automatically generate state-of-the-art-reviews. Paraphrase or textual entailment techniques cancontribute to the identification of relations across different scientific textual sources. This tutorial
provides an overview of the most relevant tasks related to the processing of scientific documents,
including but not limited to the in-depth analysis of the structure of the scientific articles, theirsemantic interpretation, content extraction and summarization.

1

Introduction

During the last decade the amount of scientific information available on-line increased at an unprecedented rate. Recent estimates reported that a new paper is published every 20 seconds. PubMed includes
more than 26M papers with a growth rate of about 1,370 new articles per day. Elsevier Scopus andThomson Reuthers ISI Web of Knowledge respectively index more than 57 and 90 million papers. TheCornell University Library arXiv initiative provides access to over 1M e-prints from various scientificdomains. At the same time, more and more papers can be freely read on-line since they are published
as Open Access content: the full text of 27% of PubMed publications and more than 17% of the articles indexed by Scopus and ISI Web of Knowledge is available on-line for free and this percentages areconsiderably growing. The Directory of Open Access Journals, one of the most authoritative indexes ofhigh quality, Open Access, peer-reviewed publications, lists more than 9,200 journals and 2.3M papers.
Sometimes between 2017 and 2021, more than half of the global papers are expected to be published
as Open Access content. Moreover, several top conferences are making their articles freely available
through dedicated archives even before the conference takes place. Social networks are by no means
outside of this picture: research networks like ResearchGate, Academia.edu or Mendeley are rapidly
expanding, facilitating scientific information sharing.
In this scenario of scientific information overload, researchers, as well as any other interested actor,
are overwhelmed by an enormous and continuously growing number of articles to consider. Understanding recent advances in specific research fields, new methods and techniques, peer reviewing, writing andevaluation of research proposals and, in general, any activity that requires a careful and comprehensive
assessment of scientific literature has turned into an extremely complex and time-consuming task forscientists world-wide. In this context, the Natural Language Processing community plays a central rolein investigating and improving new approaches to the analysis of scientific information, thus uncovering incredible opportunities for contributions and experimentations. The extraction and integration ofinformation from scientific papers constitute a key factor for the development of rich scientific knowledge bases which can be leveraged to support structured and semantically-enabled searches, intelligent
question answering and personalized content recommendation. Summarization techniques can help toidentify the essential contents of publications thus generating automatic state of the art reviews while
paraphrase or textual entailment can contribute to identify relations across different scientific textualsources.
The objective of this tutorial is to provide a comprehensive overview of the most relevant problems
we have to face when we mine scientific literature by means of Natural Language Processing Technologies, thus identifying challenges, solutions, and opportunities for our community. In particular, weconsider approaches and tools useful to analyze and characterize a wide range of structural and semantic
peculiarities of scientific articles, including document formats, layout-dependent information, discursive
structure and networks of citations. We discuss relevant scenarios where the availability of structured,
semantically-annotated publications improves the way we benefit from scientific literature, including
article summarization, scientific content search, selection and aggregation, and publication impact assessment. Related tools, applications, datasets and publication venues are also reviewed.

2Tutorial Outline

The topics of the tutorial are described below. The tutorial will be combined with demonstrations ofexisting technologies.
1. Introduction
Overwhelmed by scientific publications (patents, research articles, tutorials, presentations, etc.)
Challenges & opportunities of scientific information overload
2. Analyzing the Structure of Scientific Publications
Available formats & contents
Retrieving textual contents from PDF publications
Document structure analysis
Patent analysis
3. Mining the Semantics of Scientific Publications
Text organization
Rhetorical structure analysis
Citation networks
Interpretation of citation purpose and polarity
4. Extracting Information from Scientific Literature
Scientific entities and their identification (names, formulas, numbers, drugs, genes, etc.)
Relation extraction problems (interactions, causal relations)
5. Summarizing Scientific Information
Classic summarization approaches to scientific document
Classification-based approaches
Citation-based approaches
Summarizing patents

6. Language Resources for Scientific Text Analysis and Representation
Available scientific corpora for experimentation
Lexical Resources in specialized domains
Ontologies for scientific information modelling
7. Social Media and Science: new Opportunities
Socially connected scientific entities
Social Media metrics to assess research impact
8. Applications, Challenges and Projects
Scientific literature on-line portals
Discussion venues and challenges
Relevant projects

3Tutorial Web Site

More details on this tutorial can be accessed on-line at: http://taln.upf.edu/pages/
coling2016tutorial/.

Acknowledgments
We acknowledge (partial) support by the Spanish Ministry of Economy and Competitiveness under theMaria de Maeztu Units of Excellence Programme (MDM-2015-0502) and the European Project Dr.
Inventor (FP7-ICT-2013.8.1 - Grant no: 611383).