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Measuring Innovation in Speech and Language Processing Publications.

The goal of this paper is to propose measures of innovation through the study of publications in the field of speech and language processing. It is based on the NLP4NLP corpus, which contains the articles published in major conferences and journals related to speech and language processing over 50 years (1965-2015). It represents 65,003 documents from 34 different sources, conferences and journals, published by 48,894 different authors in 558 events, for a total of more than 270 million words and 324,422 bibliographical references. The data was obtained in textual form or as an image that had to be converted into text. This resulted in a lower quality for the most ancient papers, that we measured through the computation of an unknown word ratio. The multi-word technical terms were automatically extracted after parsing, using a set of general language text corpora. The occurrences, frequencies, existences and presences of the terms were then computed overall, for each year and for each document. It resulted in a list of 3.5 million different terms and 24 million term occurrences. The evolution of the research topics over the year, as reflected by the terms presence, was then computed and we propose a measure of the topic popularity based on this computation. The author(s) who introduced the terms were searched for, together with the year when the term was first introduced and the publication where it was introduced. We then studied the global and evolutional contributions of authors to a given topic. We also studied the global and evolutional contributions of the various publications to a given topic. We finally propose a measure of innovativeness for authors and publications.