Semantic Annotation of the ACL Anthology Corpus for the Automatic Analysis of Scientific Literature

Semantic Annotation of the ACL Anthology Corpus for the Automatic Analysis of Scientific Literature Kata Gábor∗, Haïfa Zargayouna∗, Davide Buscaldi∗, Isabelle Tellier†, Thierry Charnois∗ ∗ LIPN, CNRS (UMR 7030), Université Paris 13 Sorbonne Paris Cité † LaTTiCe (UMR 8094), CNRS, ENS Paris, Université Sorbonne Nouvelle - Paris 3 PSL Research University, Université Sorbonne Paris Cité (gabor,haifa.zargayouna,davide.buscaldi,thierry.charnois)@lipn.univ-paris13.fr, isabelle.tellier@univ-paris3.fr Abstract This paper describes the process of creating a corpus annotated for concepts and semantic relations in the scientific domain. A part of the ACL Anthology Corpus was selected for annotation, but the annotation process itself is not specific to the computational linguistics domain and could be applied to any scientific corpus. Concepts were identified and annotated fully automatically, based on a combination of terminology extraction and available ontological resources. A typology of semantic relations between concepts is also proposed. This typology, consisting of 18 domain-specific and 3 generic relations, is the result of a corpus-based investigation of the text sequences occurring between concepts in sentences. A sample of 500 abstracts from the corpus is currently being manually annotated with these semantic relations. Only explicit relations are taken into account, so that the data could serve to train or evaluate pattern-based semantic relation classification systems. Keywords: semantic annotation, semantic relations, ACL Anthology 1. Introduction One of the emerging trends of natural language technolo- gies is their use for the humanities and sciences. There is a constant increase in the production of scientific papers and experts are faced with an explosion of information that makes it difficult to have an overview of the state of the art in a given domain (Larsen and von Ins, 2010). This phe- nomenon gave rise to efforts from the semantic web, sci- entometry and natural language processing communities, trying to improve access to scientific literature. Most of these works concentrate on unfolding links between pa- pers in order to build cartographies and analyze scientific networks based on bibliographic references and topic tax- onomies (Osborne and Motta, 2012; Osborne and Motta, 2014). Shotton (2010) designed an ontology of different types of citations, while Presutti et al. (2014) aim to im- prove access to semantic content and inter-document navi- gation by identifying links between documents. Other stud- ies focus on the evolution of a scientific domain over time (Chavalarias and Cointet, 2013; Omodei et al., 2014). As opposed to these lines of research, our analysis focuses on the semantic content of individual scientific papers instead of inter-document links and author networks. The goal of our work is to automatically build a state of the art of a scientific domain. Focusing on the abstract and the introduction, we represent the semantic content of a paper as a set of domain-specific concepts and typed relations between them. By identifying instances of concepts and domain-specific relations, we can extract the contribution of a research paper. Applying this method to a corpus of papers, we can lay the foundation to applications for visualizing the evolution of a domain, the emergence of topics over time …

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