{"paper":{"title":"Efficiently Inducing Features of Conditional Random Fields","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Andrew McCallum","submitted_at":"2012-10-19T15:06:52Z","abstract_excerpt":"Conditional Random Fields (CRFs) are undirected graphical models, a special     case of which correspond to conditionally-trained finite state machines. A key     advantage of these models is their great flexibility to include a wide array of     overlapping, multi-granularity, non-independent features of the input. In face     of this freedom, an important question that remains is, what features should be     used? This paper presents a feature induction method for CRFs. Founded on the     principle of constructing only those feature conjunctions that significantly     increase log-likelihood"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1212.2504","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}