Das Blog der TIB – Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek
Welche vorbereitenden Schritte sind eigentlich notwendig, um Texte für Computer lesbar und sogar „verstehbar“ zu machen? Am Beispiel unseres neuen Prototypen TrenDTF Search geben wir einen kleinen Einblick in den Maschinenraum der Forschung und Entwicklung zu natural language processing an der TIB.
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Discourse takes various modalities, structures, and mediums. Among the commonly experienced mediums are face-to-face chats, telephone conversations, television news broadcasts, radio news, talk shows, lectures, books, and scientific articles. Intriguingly, each of these forms of discourse follow a logical structural nuance depending on the medium. The advancement of the digital age including social media communication has further led to expansion of logical discourse structures. These include blog-posts, emails, websites, review sites of products, hotels, restaurants or movies, and, finally, social media streams such as Twitter, Facebook, Reddit, #slack channels, Q&A portals such as Quora or Stack Overflow, etc., with a growing list as new mediums of communication over the World Wide Web are invented.
Large and varied streams of natural discourse only mean an even larger body of research questions remains to be explored!
NLP technology is all-pervasive for commonsense knowledge. There are many causes for this. Most of the internet and its data is about commonsense knowledge and world events, so NLP technology is developed over the data domain that is most easily available. But what about the scholarly domain with its rapidly growing body of knowledge produced worldwide? These are those specialized domains of knowledge in Science, Technology, Engineering, and Mathematics (STEM), all of which open up countless doors for NLP.
The opportunities are endless!
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In the tipping scale of activities toward the digitalization of scholarly articles, next-generation digital library frameworks such as the Open Research Knowledge Graph, targeting scholarly contributions’ highlights, are already here! We have created the NLPContributionGraph Shared Task that formalizes the building of such a scholarly contributions-focused graph over Natural Language Processing articles as an automated task. If you are eager to build a machine learner, we have the annotated scholarly contributions’ graph data for you—come join us in this endeavor!
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