Bootstrapping a Multilingual Part-of-speech Tagger in One Person-day

Bootstrapping a Multilingual Part-of-speech Tagger in One Person-day Silviu Cucerzan and David Yarowsky Department of Computer Science and Center for Language and Speech Processing Johns Hopkins University Baltimore, MD 21218 USA {silviu,yarowsky}@cs.jhu.edu Abstract This paper presents a method for bootstrapping a fine-grained, broad-coverage part-of-speech (POS) tagger in a new language using only one person- day of data acquisition effort. It requires only three resources, which are currently readily available in 60-100 world languages: (1) an online or hard-copy pocket-sized bilingual dictionary, (2) a basic library reference grammar, and (3) access to an existing monolingual text corpus in the language. The al- gorithm begins by inducing initial lexical POS dis- tributions from English translations in a bilingual dictionary without POS tags. It handles irregular, regular and semi-regular morphology through a ro- bust generative model using weighted Levenshtein alignments. Unsupervised induction of grammatical gender is performed via global modeling of context- window feature agreement. Using a combination of these and other evidence sources, interactive train- ing of context and lexical prior models are accom- plished for fine-grained POS tag spaces. Experi- ments show high accuracy, fine-grained tag resolu- tion with minimal new human effort. 1 Introduction Previous work in minimally supervised language learning has defined minimal using several different criteria. Some have assumed only partially tagged training corpora (Merialdo, 1994), while others have begin with small tagged seed wordlists (such as Collins and Singer (1999) and Cucerzan and Yarowsky (1999) for named-entity tagging). Oth- ers have exploited the automatic transfer of some already existing annotated resource in a different medium or language (such as the translingual pro- jection of part-of-speech tags, syntactic bracket- ing and inflectional morphology in Yarowsky et al. (2001), requiring no direct supervision in the for- eign language). Ngai and Yarowsky (2000) ob- served that an often more practical measure of the degree of supervision is not simply the quantity of annotated words, but the total weighted human la- bor and resource costs of different modes of su- pervision (allowing manual rule writing to be com- pared directly with active learning on a common cost-performance learning curve). In this paper we observe that another useful mea- sure of (minimal) supervision is the additional cost of obtaining a desired functionality from existing commonly available knowledge sources. In particu- lar, we note that for a remarkably wide range of lan- guages, academic libraries, many booksellers and websites offer a foundation of linguistic wisdom in reference grammars and dictionaries. Thus starting from this baseline, what is the marginal cost of dis- tilling from and augmenting this existing knowledge to achieve a desired new task functionality? 2 Inducing POS Tag Candidates from Unlabeled Bilingual Dictionaries A substantial percentage of foreign language dic- tionaries that are available on line or in smaller pa- perback format are simple bilingual word or phrase translation lists which fail to specify part of speech.1 Thus one component question of this work is how can one extract preliminary part-of-speech distribu- tions from untagged monolingual translation lists. Figure 1 illustrates …

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