Large-Scale Semi-Supervised Learning for Natural Language ...

anaphoricity by [Denis and Baldridge, 2007].The suitability of this kind of approach to correcting some of our system’s errors is especiallyobvious when we inspect the probabilities of the maximum entropy model’s outputdecisions on the test set. Where the maximum entropy classifier makes mistakes, it does sowith less confidence than when it classifies correct examples. The average predicted probabilityof the incorrect classifications is 76.0% while the average probability of the correctclassifications is 90.3%. Many incorrect decisions are ready to switch sides; our next stepwill be to use features based on the preceding discourse and the candidate antecedents tohelp give the incorrect classifications a helpful push.3.8 ConclusionWe proposed a unified view of using web-scale N-gram models for lexical disambiguation.State-of-the-art results by our supervised and unsupervised systems demonstrate that it isnot only important to use the largest corpus, but to get maximum information from thiscorpus. Using the Google 5-gram data not only provides better accuracy than using pagecounts from a search engine, but facilitates the use of more context of various sizes andpositions. The TRIGRAM approach, popularized by Lapata and Keller [2005], clearly underperformsthe unsupervised SUMLM system on all three applications.In each of our tasks, the candidate set was pre-defined, and training data was availableto train the supervised system. While SUPERLM achieves the highest performance, thesimpler SUMLM, which uses uniform weights, performs nearly as well as SUPERLM, andexceeds it for less training data. Unlike SUPERLM, SUMLM could easily be used in caseswhere the candidate sets are generated dynamically; for example, to assess the contextualcompatibility of preceding-noun candidates for anaphora resolution.54