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We describe an annotation initiative to capture the scholarly contributionsin natural language processing (NLP) articles, particularly, for the articlesthat discuss machine learning (ML) approaches for various informationextraction tasks. We develop the annotation task based on a pilot annotationexercise on 50 NLP-ML scholarly articles presenting contributions to fiveinformation extraction tasks 1. machine translation, 2. named entityrecognition, 3. question answering, 4. relation classification, and 5. textclassification. In this article, we describe the outcomes of this pilotannotation phase. Through the exercise we have obtained an annotationmethodology; and found ten core information units that reflect the contributionof the NLP-ML scholarly investigations. The resulting annotation scheme wedeveloped based on these information units is called NLPContributions.  The overarching goal of our endeavor is four-fold: 1) to find a systematicset of patterns of subject-predicate-object statements for the semanticstructuring of scholarly contributions that are more or less genericallyapplicable for NLP-ML research articles; 2) to apply the discovered patterns inthe creation of a larger annotated dataset for training machine readers ofresearch contributions; 3) to ingest the dataset into the Open ResearchKnowledge Graph (ORKG) infrastructure as a showcase for creating user-friendlystate-of-the-art overviews; 4) to integrate the machine readers into the ORKGto assist users in the manual curation of their respective articlecontributions. We envision that the NLPContributions methodology engenders awider discussion on the topic toward its further refinement and development.Our pilot annotated dataset of 50 NLP-ML scholarly articles according to theNLPContributions scheme is openly available to the research community athttps://doi.org/10.25835/0019761.

NLPContributions: An Annotation Scheme for Machine Reading of Scholarly Contributions in Natural Language Processing Literature