방법 비교

	Mean Average Precision (MAP)	Citation Context and Sentiment Analysis
분야	계량서지학	계량서지학
계열	Process / pipeline	Process / pipeline
기원 연도	2000	2006
창시자	TREC / information-retrieval evaluation community; Chris Buckley & Ellen Voorhees (stability analysis)	Simone Teufel, Advaith Siddharthan & Dan Tidhar (citation function); Awais Athar (citation sentiment)
유형	Binary-relevance ranked-retrieval evaluation pipeline	NLP pipeline for classifying the rhetorical function and polarity of citations
원전	Buckley, C., & Voorhees, E. M. (2000). Evaluating evaluation measure stability. In Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '00), 33-40.	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.
별칭	MAP, Average Precision, AP, Mean AP	Citation Function Classification, Citation Polarity Analysis, Citation Sentiment Detection, Citation Context Mining
관련	3	3
요약	Mean Average Precision (MAP) is the classic single-number summary of ranked-retrieval effectiveness under binary relevance and the headline metric of the TREC ad hoc retrieval tracks. For a single query, average precision (AP) computes the precision of the result list at each rank where a relevant document appears and averages those values, rewarding systems that rank all relevant documents highly; MAP is then the mean of AP across a set of queries. Buckley and Voorhees's 2000 SIGIR analysis of evaluation-measure stability showed that average precision is among the most stable and discriminating IR measures, requiring fewer queries than alternatives like precision at a fixed cutoff to reliably tell two systems apart. MAP remains a standard reporting metric for ranked retrieval, complementing graded-relevance measures such as nDCG.	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.