Feature-Rich Part-of-speech Tagging for Morphologically Complex Languages: Application to Bulgarian

Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 492–502, Avignon, France, April 23 - 27 2012. c⃝2012 Association for Computational Linguistics Feature-Rich Part-of-speech Tagging for Morphologically Complex Languages: Application to Bulgarian Georgi Georgiev and Valentin Zhikov Ontotext AD 135 Tsarigradsko Sh., Sofia, Bulgaria {georgi.georgiev,valentin.zhikov}@ontotext.com Petya Osenova and Kiril Simov IICT, Bulgarian Academy of Sciences 25A Acad. G. Bonchev, Sofia, Bulgaria {petya,kivs}@bultreebank.org Preslav Nakov Qatar Computing Research Institute, Qatar Foundation Tornado Tower, floor 10, P.O. Box 5825, Doha, Qatar pnakov@qf.org.qa Abstract We present experiments with part-of- speech tagging for Bulgarian, a Slavic lan- guage with rich inflectional and deriva- tional morphology. Unlike most previous work, which has used a small number of grammatical categories, we work with 680 morpho-syntactic tags. We combine a large morphological lexicon with prior linguis- tic knowledge and guided learning from a POS-annotated corpus, achieving accuracy of 97.98%, which is a significant improve- ment over the state-of-the-art for Bulgarian. 1 Introduction Part-of-speech (POS) tagging is the task of as- signing each of the words in a given piece of text a contextually suitable grammatical category. This is not trivial since words can play different syn- tactic roles in different contexts, e.g., can is a noun in “I opened a can of coke.” but a verb in “I can write.” Traditionally, linguists have classi- fied English words into the following eight basic POS categories: noun, pronoun, adjective, verb, adverb, preposition, conjunction, and interjection; this list is often extended a bit, e.g., with deter- miners, particles, participles, etc., but the number of categories considered is rarely more than 15. Computational linguistics works with a larger inventory of POS tags, e.g., the Penn Treebank (Marcus et al., 1993) uses 48 tags: 36 for part- of-speech, and 12 for punctuation and currency symbols. This increase in the number of tags is partially due to finer granularity, e.g., there are special tags for determiners, particles, modal verbs, cardinal numbers, foreign words, existen- tial there, etc., but also to the desire to encode morphological information as part of the tags. For example, there are six tags for verbs in the Penn Treebank: VB (verb, base form; e.g., sing), VBD (verb, past tense; e.g., sang), VBG (verb, gerund or present participle; e.g., singing), VBN (verb, past participle; e.g., sung) VBP (verb, non- 3rd person singular present; e.g., sing), and VBZ (verb, 3rd person singular present; e.g., sings); these tags are morpho-syntactic in nature. Other corpora have used even larger tagsets, e.g., the Brown corpus (Kuˇcera and Francis, 1967) and the Lancaster-Oslo/Bergen (LOB) corpus (Johansson et al., 1986) use 87 and 135 tags, respectively. POS tagging poses major challenges for mor- phologically complex languages, whose tagsets encode a lot of additional morpho-syntactic fea- tures (for most of the basic POS categories), e.g., gender, number, person, etc. For example, the BulTreeBank (Simov et al., 2004) for Bulgarian uses 680 tags, while the Prague Dependency Tree- bank (Hajiˇc, 1998) for Czech has over 1,400 tags. Below we present experiments …

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