手法を比較

	BM25 Probabilistic Ranking (Okapi)	Citation Context and Sentiment Analysis
分野	計量書誌学	計量書誌学
系統	Process / pipeline	Process / pipeline
提唱年	2009	2006
提唱者	Stephen Robertson; Karen Spärck Jones; Hugo Zaragoza (Okapi team, City University London)	Simone Teufel, Advaith Siddharthan & Dan Tidhar (citation function); Awais Athar (citation sentiment)
種類	Probabilistic term-weighting and document-scoring pipeline for ranked retrieval	NLP pipeline for classifying the rhetorical function and polarity of citations
原典	Robertson, S., & Zaragoza, H. (2009). The Probabilistic Relevance Framework: BM25 and Beyond. Foundations and Trends in Information Retrieval, 3(4), 333-389.	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.
別名	Okapi BM25, Best Matching 25, Probabilistic Relevance Ranking, BM25 Term Weighting	Citation Function Classification, Citation Polarity Analysis, Citation Sentiment Detection, Citation Context Mining
関連	3	3
概要	BM25, the Okapi 'Best Matching 25' function, is the dominant classical ranking function in information retrieval and the workhorse term-weighting scheme behind most lexical search engines and bibliographic databases. Developed by Stephen Robertson, Karen Spärck Jones and colleagues at City University London and formalized in Robertson and Zaragoza's 2009 monograph on the Probabilistic Relevance Framework, BM25 scores a document against a query as a sum, over query terms, of inverse-document-frequency weights multiplied by a saturating, length-normalized transform of within-document term frequency. Two free parameters control how quickly repeated terms stop adding evidence (k1) and how strongly document length is penalized (b). BM25 consistently outperformed plain TF-IDF in the TREC evaluations and remains the standard first-stage retrieval baseline against which modern neural rankers are measured.	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.