Brill, CL 1995

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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

Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging


This paper introduces a learning technique called "Transformation-based error-driven learning", a.k.a. Transformation Based Learning (TBL).

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). Some advantages of Transformation based learning include the following: simple conceptually, TBL can be adapted to different learning problems, rich triggers/rules can make use of specific information and context, seemingly resistant to over-fitting(observed empirically, not entirely understood). When using Transformation based learning, a number of things should be considered: when constructing all possible transformations should we manually create rules or make templates?; the potentially huge search space can be problematic so you may need to use linguistic intuition to limit space; there are 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).

The authors described the use of Transformation Based Learning on POS Tagging. 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.

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

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

  • Text Chunking using Transformation-Based Learning: Transformation-based learning applied to Noun-Phrase Chunking - Ramshaw_&_Marcus,_1995.
  • Tagging gene and protein names in biomedical text: Transformation-based learning applied to gene & protein tagging, for system called Abgene - Tanabe_&_Wilbur,_2002.
  • Sense Deduction: The Power of Peewees Applied to SENSEVAL-2 Sweedish Lexical Sample Task: Transformation-based learning applied to word sense disambiguation - Lager_&_Zinovjeva,_2001.