Tagset Reductions in Morphosyntactic Tagging of Croatian Texts Željko Agić, Marko Tadić and Zdravko Dovedan University of Zagreb {zagic, mtadic,

Introduction morphosyntactic tagging asssigning word categories and subcategories to words in sentence context issues modelling sentence context handling unknown words, dealing with sparse data common approaches rule-based, stochastic, hybrid data-driven models are predominant today best performing taggers are based on SVM, CRF, HMM

Introduction data-driven tagging modules the tagger and the data data implies tagset encoding word (sub)categories a solved problem? state-of-the-art accuracy on English is 97-98% tagsets for English max. 100 different tags 1475 different morphosyntactic tags used in the Croatian Morphological Lexicon accuracy for state-of-the art taggers drops by ca 10%

Tagging Croatian texts CroTag tagger inspired by TnT and HunPos trained on manually MTE v3 annotated 118 kw corpus accuracy identical to these (96-97% EN, 85-86% HR) all are highly dependent on unknown word counts improvements using the inflectional lexicon to handle unknown words tagger voting, hibridization?

From another perspective... goals of tagging reaching perfect accuracy on full tagset or making large-scale NLP systems perform better? specific requirements users and systems always have them example: named entity normalization in Croatian Is it Ivo (m.) or Iva (f.) Sanader? specific tasks may require specific tagset design keeping speed and memory footprint reducing tagset size means raising accuracy

Reducing the tagset MulText East version 3 positional tagset, letters encode categories example: Ncmsn = noun, common, masculine, etc. the subsets 1 – strip non-inflective categories and numerals (800 tags) 2 – strip verbs (739) 3 – strip all but gender, number, case and noun type (243) 4 – remove case category (48) 5 – keep noun type category only (15) 6 – maintain part-of-speech information only (13)

Results

More results adjectives, nouns and pronouns most difficultly tagged cattegories for Croatian combination of frequency and tags used maybe these are most important to tag accurately? F1-measures on adjectives, nouns and pronouns typesubset 0subset 4subset 5 Adjective0.64± ± ±0.02 Noun0.79± ± ±0.01 Pronoun0.76± ± ±0.01

Conclusions results are as expected reducing tagset size raises tagging accuracy sacrificing information for efficiency reductions are illustrative careful tagset design required with regards to requirements further work as mentioned: reaching perfect accuracy on full tagset or making large-scale NLP systems perform better?

Your questions? Computational Linguistic Models and Language Technologies for Croatian rmjt.ffzg.hr | hml.ffzg.hr | hnk.ffzg.hr

Tagset Reductions in Morphosyntactic Tagging of Croatian Texts Željko Agić, Marko Tadić and Zdravko Dovedan University of Zagreb {zagic, mtadic,