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== Citation == Brill, E. 1995. Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging. Computational Linguistics. 21. 4. p543-565 == Online version == [http://www.ldc.upenn.edu/acl/J/J95/J95-4004.pdf Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging] == Summary == This [[Category::paper]] introduces a learning technique called "Transformation-based error-driven learning", a.k.a. [[UsesMethod::Transformation Based Learning]] (TBL). The key points from the paper are: * Transformation Based Learning is an algorithm that learns a sequence of transformations to improve tagging on some baseline tagger * Transformations are broken down into two components: a ''triggering event'' (such as if the previous word is a determiner) and a ''re-write rule'' (such as change tag from modal to noun) * Authors described the use of Transformation Based Learning on [[AddressesProblem::POS Tagging]]. ** '''Bottom-line of results''': When compared to a markov-model based POS tagger, the TBL Tagger was able to achieve comparable tagging accuracy with a number of rules which is much smaller than the number of context probabilities calculated for stocastic tagger, and can do so with a much smaller sized training corpus. Also initial rules contribute most to tagging accuracy (say first 100 or 200), and rest improve performance marginally. * Some advantages of Transformation based learning: ** Simple conceptually ** Can be adapted to different learning problems ** Rich triggers/rules that can make use of specific information and context ** Seemingly resistant to over-fitting *** Empirical result, not entirely understood *** Always learn on whole data set * Some considerations: ** Constructing all possible transformations: manually create rules or make templates?, potentially huge search space can be problematic, may need to use linguistic intuition to limit space ** No probabilities/confidence associated with results ** Transformations in one environment could affect application in another: should transformations be applied immediately or only after entire corpus is examined for triggering conditions? what order do we process corpus? (left-to-right or right-to-left) == Transformation-Based Learning == '''The learning algorithm is summarized as follows''' (see Figure 1 from paper as well): * Pass un-annotated corpus (training data) through initial-state annotator * Compare against truth to get current score (based on number of classification errors) * Loop until no transformation can be found to improve score (Greedy search) ** Consider all transformations rules applied to training data, select best ** Apply to transformation to data & get current score ** Add transformation to ordered transformation list [[File:brill95_fig1.png]] '''Transformations are applied as follows''': * Run initial-state annotator on unseen data * Loop through ordered list of transformations, and apply each transformation. == Example usage: POS Tagging == * Initial-state: ** Each word assigned to most likely POS tag based on training corpus * Rules: ** Non-lexical: Based mainly on tags of words located near the target position ** Lexical: Based mainly on words in surrounding context == Related papers == * '''SEARN in Practice''': This unpublished manuscript showcases three example problems where SEARN can be used - [[RelatedPaper::Daume_et_al,_2006]].

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