Sammenlign metoder

	Normalized Discounted Cumulative Gain (nDCG)	Citation Context and Sentiment Analysis
Fagområde	Bibliometri	Bibliometri
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	2002	2006
Ophavsperson	Kalervo Järvelin & Jaana Kekäläinen	Simone Teufel, Advaith Siddharthan & Dan Tidhar (citation function); Awais Athar (citation sentiment)
Type	Graded-relevance ranking-evaluation pipeline with position discounting and normalization	NLP pipeline for classifying the rhetorical function and polarity of citations
Oprindelig kilde	Järvelin, K., & Kekäläinen, J. (2002). Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems, 20(4), 422-446.	Teufel, S., Siddharthan, A., & Tidhar, D. (2006). Automatic classification of citation function. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), 103-110.
Aliasser	nDCG, Discounted Cumulative Gain, DCG/IDCG Normalization, Cumulated Gain Evaluation	Citation Function Classification, Citation Polarity Analysis, Citation Sentiment Detection, Citation Context Mining
Relaterede	3	3
Resumé	Normalized Discounted Cumulative Gain (nDCG) is the standard metric for evaluating ranked retrieval and recommendation when relevance comes in grades rather than a simple relevant/non-relevant binary. Introduced by Kalervo Järvelin and Jaana Kekäläinen in their 2002 ACM Transactions on Information Systems paper on cumulated gain-based evaluation, nDCG rewards a system for placing highly relevant documents near the top of the ranking. It accumulates the graded relevance ('gain') of each retrieved item, discounts that gain by how far down the list the item sits, and normalizes the total against the best possible ordering so that scores fall on a comparable 0-to-1 scale across queries. Because it handles multi-level relevance and is rank-sensitive, nDCG has become the dominant effectiveness measure for web search, learning-to-rank, and academic-search evaluation.	Citation context and sentiment analysis is the scientometric text-mining technique that reads the words around a citation to recover why one paper cites another and with what attitude. Standard citation counting treats every citation as an equal, polarity-free vote, but Simone Teufel, Advaith Siddharthan and Dan Tidhar's 2006 EMNLP work showed that citations serve distinct rhetorical functions — using a method, contrasting with prior work, acknowledging a basis, or merely referencing in passing — and that these functions can be classified automatically from the citing sentence. Awais Athar's 2011 work extended this to sentiment, distinguishing positive, neutral, and negative (critical) citations using sentence-structure features. Together these methods turn the raw citation graph into a typed, sentiment-bearing graph, enabling more meaningful impact measures, better citation indexers, and summaries of how a paper has been received.