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

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 t...

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Bibliographic Details
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 - ENS Paris (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: Other/Unknown Material
Language:English
Published: HAL CCSD 2014
Subjects:
ACL Anthology
semantic network
social network
socio
lang
Iceland
Online Access:https://hal.archives-ouvertes.fr/hal-01056147/file/paper_lrec.pdf
https://hal.archives-ouvertes.fr/hal-01056147
Description
Summary: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.

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