Adaptive Transformation-Based Learning for Improving Dictionary Tagging

Adaptive Transformation-based Learning for Improving Dictionary Tagging Burcu Karagol-Ayan, David Doermann, and Amy Weinberg Institute for Advanced Computer Studies (UMIACS) University of Maryland College Park, MD 20742 {burcu,doermann,weinberg}@umiacs.umd.edu Abstract We present an adaptive technique that en- ables users to produce a high quality dic- tionary parsed into its lexicographic com- ponents (headwords, pronunciations, parts of speech, translations, etc.) using an extremely small amount of user provided training data. We use transformation- based learning (TBL) as a postprocessor at two points in our system to improve per- formance. The results using two dictio- naries show that the tagging accuracy in- creases from 83% and 91% to 93% and 94% for individual words or “tokens”, and from 64% and 83% to 90% and 93% for contiguous “phrases” such as definitions or examples of usage. 1 Introduction The availability and use of electronic resources such as electronic dictionaries has increased tre- mendously in recent years and their use in Natural Language Processing (NLP) systems is widespread. For languages with limited electronic resources, i.e. low-density languages, however, we cannot use automated techniques based on par- allel corpora (Gale and Church, 1991; Melamed, 2000; Resnik, 1999; Utsuro et al., 2002), compa- rable corpora (Fung and Yee, 1998), or multilin- gual thesauri (Vossen, 1998). Yet for these low- density languages, printed bilingual dictionaries often offer effective mapping from the low-density language to a high-density language, such as En- glish. Dictionaries can have different formats and can provide a variety of information. However, they typically have a consistent layout of entries and a 1 Headword 5 Translation 2 POS 6 Example of usage 3 Sense number 7 Example of usage translation 4 Synonym 8 Subcategorization Figure 1: Sample tagged dictionary entries. Eight tags are identified and tagged in the given entries. consistent structure within entries. Publishers of dictionaries often use a combination of features to impose this structure including (1) changes in font style, font-size, etc. that make implicit the lexico- graphic information1, such as headwords, pronun- ciations, parts of speech (POS), and translations, (2) keywords that provide an explicit interpreta- tion of the lexicographic information, and (3) var- ious separators that impose an overall structure on the entry. For example, a boldface font may in- dicate a headword, italics may indicate an exam- ple of usage, keywords may designate the POS, commas may separate different translations, and a numbering system may identify different senses of a word. We developed an entry tagging system that rec- ognizes, parses, and tags the entries of a printed dictionary to reproduce the representation elec- tronically (Karagol-Ayan et al., 2003). The sys- tem aims to use features as described above and the consistent layout and structure of the dictio- 1For the purposes of this paper, we will refer to the lexi- cographic information as tag when necessary. 257 naries to capture and recover the lexicographic in- formation in the entries. Each token2 or group of tokens (phrase)3 in an entry associates with a tag indicating its lexicographic information in the en- …

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