Mapping the Natural Language Processing Domain: Experiments using the ACL Anthology

• Main Authors: Omodei, Elisa, Cointet, Jean-Philippe, Poibeau, Thierry
• Other Authors: Lattice - Langues, Textes, Traitements informatiques, Cognition - UMR 8094 (Lattice), Université Sorbonne Nouvelle - Paris 3-Université Sorbonne Paris Cité (USPC)-Centre National de la Recherche Scientifique (CNRS)-Université Paris sciences et lettres (PSL)-Département Littératures et langage (LILA), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL), Sciences en Société (SenS), Institut National de la Recherche Agronomique (INRA), United Nations Educational Scientific and Cultural Organization (UNESCO). INT.
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2014
• Subjects: ACL Anthology; semantic network; social network; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [SCCO.COMP]Cognitive science/Computer science; [SCCO.LING]Cognitive science/Linguistics; [SHS.LANGUE]Humanities and Social Sciences/Linguistics; [SHS.INFO]Humanities and Social Sciences/Library and information sciences; Iceland
• Online Access: https://hal.archives-ouvertes.fr/hal-01056147; https://hal.archives-ouvertes.fr/hal-01056147/document; https://hal.archives-ouvertes.fr/hal-01056147/file/paper_lrec.pdf

International audience This paper investigates the evolution of the computational linguistics domain through a quantitative analysis of the ACL Anthology (containing around 12,000 papers published between 1985 and 2008). Our approach combines complex system methods with natural language processing techniques. We reconstruct the socio-semantic landscape of the domain by inferring a co-authorship and a semantic network from the analysis of the corpus. First, keywords are extracted using a hybrid approach mixing linguistic patterns with statistical information. Then, the semantic network is built using a co-occurrence analysis of these keywords within the corpus. Combining temporal and network analysis techniques, we are able to examine the main evolutions of the field and the more active subfields over time. Lastly we propose a model to explore the mutual influence of the social and the semantic network over time, leading to a socio-semantic co-evolutionary system.