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In this paper, we consider sentences that contain multiple in-text references (MIR) and their position in the rhetorical structure of articles. We carry out the analysis of MIR in a large-scale dataset of about 80,000 research articles published by the Public Library of Science in 7 journals. We analyze two major characteristics of MIR: their positions in the IMRaD structure of articles and the number of in-text references that make up a MIR in the different journals. We show that MIR are rather frequent in all sections of the rhetorical structure. In the Introduction section, sentences containing MIR account for more than half of the sentences with references. We examine the syntactic patterns that are most used in the contexts of both multiple and single in-text references and show that they are composed, for the most part, of noun groups. We point out the specificity of the Methods section in this respect.

International audience

International audience; This paper addresses the problem of building recommender systems for scientific papers based on the linguistic and contextual analysis of citation contexts. We explain the importance of taking into consideration citation contexts and the different methodologies that exist as well as the ways that citations impact recommender systems. We also discuss the limits of using citation contexts to generate recommendations.

The organization of scientific papers typically follows a standardized pattern, the well-known IMRaD structure introduction, methods, results, and discussion. Using the full text of 45,000 papers published in the PLoS series of journals as a case study, this paper investigates, from the viewpoint of bibliometrics, how references are distributed along the structure of scientific papers as well as the age of these cited references. Once the sections of articles are realigned to follow the IMRaD sequence, the position of cited references along the text of articles is invariant across all PLoS journals, with the introduction and discussion accounting for most of the references. It also provides evidence that the age of cited references varies by section, with older references being found in the methods and more recent references in the discussion. These results provide insight into the different roles citations have in the scholarly communication process.

EC3 is intended to extract relevant information from large heterogeneous and multilingual text data, in particular in WEB 3.0. The project is based on an original method: contextual exploration. EC3 does not need syntactic analysis, statistical analysis or a "general" ontology. EC3 uses only small ontologies called "linguistic ontologies" that express the linguistic knowledge of a user who must concentrate on the relevant information from one point of view. This is why EC3 works very quickly on large corpus, whose components can be both whole books as well as "short texts": SMS to books. At the output, EC3 offers a visual representation of information using an original approach: the "Memory Islands". EC3 is implanted in the ACASA / LIP6 team. EC3 is tested on the large digitized corpus provided by the Labex OBVIL «Observatoire de la Vie Litteraire», in partnership with the Bibliotheque Nationale de France (http://obvil.paris-sorbonne.fr/). OBVIL intends to develop all the resources offered by digitization and computer applications to examine French literature from the sixteenth to the twentieth century, English and American literature, Italian literature, Spanish literature, in its most traditional formats and media Or the most innovative.

Using the full-text corpus of more than 75,000 research articles published by seven PLOS journals, this paper proposes a natural language processing approach for identifying the function of citations. Citation contexts are assigned based on the frequency of n-gram co-occurrences located near the citations. Results show that the most frequent linguistic patterns found in the citation contexts of papers vary according to their location in the IMRaD structure of scientific articles. The presence of negative citations is also dependent on this structure. This methodology offers new perspectives to locate these discursive forms according to the rhetorical structure of scientific articles, and will lead to a better understanding of the use of citations in scientific articles.

Scientific abstracts contain what is considered by the author(s) as information that best describe documents' content. They represent a compressed view of the informational content of a document and allow readers to evaluate the relevance of the document to a particular information need. However, little is known on their composition. This paper contributes to the understanding of the structure of abstracts, by comparing similarity between scientific abstracts and the text content of research articles. More specifically, using sentence-based similarity metrics, we quantify the phenomenon of text re-use in abstracts and examine the positions of the sentences that are similar to sentences in abstracts in the IMRaD structure (Introduction, Methods, Results and Discussion), using a corpus of over 85,000 research articles published in the seven PLOS journals. We provide evidence that 84% of abstract have at least one sentence in common with the body of the article. Our results also show that the sections of the paper from which abstract sentence are taken are invariant across the PLOS journals, with sentences mainly coming from the beginning of the introduction and the end of the conclusion., Comment: Preprint: Journal of Documentation, vol. 72, issue 4. Submitted 12-Sep-2015, accepted 10-Feb-2016. 13 pages

In this paper we study the properties of recurring in-text references in research articles and more specifically their positions in the rhetorical structure of articles and their age with respect to the citing article. We have processed a large scale corpus of approximately 80,000 papers published by PLOS (Public Library of Science). We examine the number and types of recurring in-text references, as well as their age according to positions in the IMRaD structure. (Introduction, Methods, Results, and Discussion). The results show that the age of recurring references varies considerably in all sections and journals. While they are especially dense in the Introduction section, most of them reappear in the beginning of the Results and the Discussion sections. We also observe that the beginning of the Methods and the Results sections share a significant number of recurring references.

International audience; We propose a hybrid method for the extraction and characterization of citations in scientific papers using machine learning combined with rule-based approaches. Our protocol consists of the extraction of metadata, bibliography parsing, section titles processing, and find-grained semantic annotation on the sentence level of texts. This allows us to generate Linked Open Data from a set of research papers in XML.

International audience; We present an Information Retrieval System for scientific publications that provides the possibility to filter results according to semantic facets. We use sentence-level semantic annotations that identify specific semantic relations in texts, such as methods, definitions, hypotheses, that correspond to common information needs related to scientific literature. The semantic annotations are obtained using a rule-based method that identifies linguistic clues organized into a linguistic ontology. The system is implemented using Solr Search Server and offers efficient search and navigation in scientific papers.

The last several years have seen the emergence of digital libraries from which documents are harvested using the OAI-PMH protocol. Considering the volume of data provided by these repositories, we are interested in the exploitation of the full text content of scientific publications. Our aim is to bring new value to scientific publications by automatic extraction and semantic analysis. The identification of bibliographic references in texts makes it possible to localize specific text segments that carry linguistic markers in order to annotate a set of semantic categories related to citations. This work uses a categorization of surface linguistic markers organized in a linguistic ontology. The semantic annotations are used to enrich the document metadata and to provide new types of visualizations in an information retrieval context. We present the system architecture as well as some experimental results.