| Author(s) | Algorithm Type | Key Features & Characteristics | Tested Results | Corpus Used | Paper(s) |
|---|---|---|---|---|---|
| Lappin S. and Lease H. | Pronominal Resolution Using Salience Measures | Operates on in-depth syntactic information. Salience-based discourse model: weighting factors include Context Recency, Subject/Direct-Object emphasis, etc. Use Equivalence Classes to maintain coreference. | Recall=85% Precision=85-87% | 5 Computer manual texts containing 82,000 words with 560 pronominal anaphors | "An Algorithm For Pronominal Anaphora Resolution" (1994) |
| Kennedy C. and Boguraev B. | Modified Lappin/Leass Model For Pronominal Resolution | Flat morpho-syntactic analysis using output of POS tagger. Less robust than Lappin/Leass's. Does not require full syntactic text parsing. Use COREF classes and Salience weights similar to Lappin/Leass. | Accuracy=75% | 27 Random Web Documents, 231/306 anaphors correctly resolved | "Anaphora in a Wider Context: Tracking Discourse Referents" |
| Srinivas B. and Baldwin B. | Super-tag Representation for Proper-noun Resolution | LTAG formalism: syntactic trees of sentences. Each word associated with a number of supertags for each syntactic configuration it may appear in. Use supertag disambiguation to select the appropriate one. Use established dependencies among supertags for resolution. | Recall=32% Precision=79% (Proper-Noun Resolution Only) | 1000 Sentences (source unknown) | "Exploiting Super tag Representation for Fast Coreference Resolution" (1996) |
| Azzam, Humphreys, Gaizauskas | Focus-Based Approach For Pronoun Resolution | Assumes that anaphor generally refer to the current discourse focus. Use focus registers and stack to keep track of states and events. Weakness: too reliant on the accuracy and completeness of grammatical information. No significant performance increase over the simple heuristic-based approach | Recall=55.4% Precision=70-75% | 30 Wall Street Journal articles, averaging 462 words. Total number of coreferences = 1627. | "Quantitative Evaluation of Coreference Algorithms in an Information Extraction System" (2000) |
| Mitkov R. | Knowledge-poor Apporach for Pronominal Resolution | Using text preprocessed by a POS tagger and some syntactic constraints to score antecedent candidates. Scope of candidate antecedents limited to 2 sentences before/after the anaphor | Accuracy=89.7% (Ratio of Precision to Recall | Random sample text from an English technical manual(141 pgs). Out of 71 pronouns, 48 are anaphorically relevant. | "Robust Pronoun Resolution With Limited Knowledge" (1998) |
| Williams S.. | Rule-Based Resolution of Non-Reference Noun Phrases (NRNP) and Reference Noun Phrases (RNP) in unrestricted text For summarization purposes. | Using rules and knowledge bases of names, titles, and General Knowledge. | Accuracy=76% (After 3 estimates) 61% (after first estimate) | collection of sentences from newspaper articles and New Scientist Journal | "Rule-Based Reference Resolution for Unrestricted Text using POS tagging and NP parsing(1996) |