Optimizing the production, maintenance and extension of lexical resources is one the crucial aspects impacting Natural Language Processing (NLP). A second aspect involves optimizing the process leading to their integration in applications. With this respect, we believe that the production of a consensual specification on multilingual lexicons can be a useful aid for the various NLP actors. Within ISO, one purpose of LMF (ISO-24613) is to define a standard for lexicons that covers multilingual data.
This paper introduces the NLP4NLP corpus, which contains articles published in 34 major conferences and journals in the field of speech and natural language processing over a period of 50 years (1965-2015), comprising 65,000 documents, gathering 50,000 authors, including 325,000 references and representing ∼270 million words. Most of these publications are in English, some are in French, German, or Russian. Some are open access, others have been provided by the publishers. In order to constitute and analyze this corpus several tools have been used or developed. Many of them use Natural Language Processing methods that have been published in the corpus, hence its name. The paper presents the corpus and some findings regarding its content (evolution over time of the number of articles and authors, collaborations between authors, citations between papers and authors), in the context of a global or comparative analysis between sources. Numerous manual corrections were necessary, which demonstrated the importance of establishing standards for uniquely identifying authors, articles, or publications.
Optimizing the production, maintenance and extension of lexical resources is one the crucial aspects impacting natural language processing (NLP). A second aspect involves optimizing the process leading to their integration in applications. With this respect, we believe that a consensual specification on monolingual, bilingual and multilingual lexicons can be a useful aid for the various
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 . 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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.