A Backoff Model for Bootstrapping Resources for Non-English Languages

Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 851–858, Vancouver, October 2005. c⃝2005 Association for Computational Linguistics A Backoff Model for Bootstrapping Resources for Non-English Languages∗ Chenhai Xi and Rebecca Hwa Department of Computer Science University of Pittsburgh {chenhai,hwa}@cs.pitt.edu Abstract The lack of annotated data is an ob- stacle to the development of many natural language processing applica- tions; the problem is especially severe when the data is non-English. Pre- vious studies suggested the possibility of acquiring resources for non-English languages by bootstrapping from high quality English NLP tools and paral- lel corpora; however, the success of these approaches seems limited for dis- similar language pairs. In this paper, we propose a novel approach of com- bining a bootstrapped resource with a small amount of manually annotated data. We compare the proposed ap- proach with other bootstrapping meth- ods in the context of training a Chinese Part-of-Speech tagger. Experimental results show that our proposed ap- proach achieves a significant improve- ment over EM and self-training and systems that are only trained on man- ual annotations. 1 Introduction Natural language applications that use super- vised learning methods require annotated train- ing data, but annotated data is scarce for many ∗We thank Stephen Clark, Roger Levy, Carol Nichols, and the three anonymous reviewers for their helpful com- ments. non-English languages. It has been suggested that annotated data for these languages might be automatically created by leveraging paral- lel corpora and high-accuracy English systems (Yarowsky and Ngai, 2001; Diab and Resnik, 2002). The studies are centered around the assumption that linguistic analyses for English (e.g., Part-of-Speech tags, Word sense disam- biguation, grammatical dependency relation- ships) are also valid analyses in the translation of the English. For example, in the English noun phrase the red apples, red modifies ap- ples; the same modifier relationship also exists in its French translations les pommes rouges, even though the word orders differ. To the extent that the assumption is true, annotated data in the non-English language can be created by pro- jecting English analyses across a word aligned parallel corpus. The resulting projected data can then serve as (albeit noisy) training exam- ples to develop applications in the non-English language. The projection approach faces both a theo- retical and a practical challenge. Theoretically, it is well-known that two languages often do not express the same meaning in the same way (Dorr, 1994). Practically, the projection frame- work is sensitive to component errors. In partic- ular, poor word alignments significantly degrade the accuracy of the projected annotations. Pre- vious research on resource projection attempts to address these problems by redistributing the parameter values (Yarowsky and Ngai, 2001) or by applying transformation rules (Hwa et al., 851 2002). Their experimental results suggest that while these techniques can overcome some er- rors, they are not sufficient for projected data that are very noisy. In this work, we tackle the same problems by relaxing the zero manual annotation constraint. The main question …

Content truncated to 500 words