Disambiguation of Morphological Structure using a PCFG

12656

Authors

nan

Abstract

German has a productive morphology and allows the creation of complex words which are often highly ambiguous. This paper reports on the development of a head-lexicalized PCFG for the disam- biguation of German morphological anal- yses. The grammar is trained on unla- beled data using the Inside-Outside algo- rithm. The parser achieves a precision of more than 68% on difficult test data, which is 23% more than the baseline ob- tained by randomly choosing one of the simplest analyses. Remarkable is the fact that precision drops to 52% without lexi- calization. 1

Keywords

nan

Full Content

Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 515–522, Vancouver, October 2005. c⃝2005 Association for Computational Linguistics Disambiguation of Morphological Structure using a PCFG Helmut Schmid Institute for Natural Language Processing (IMS) University of Stuttgart Germany schmid@ims.uni-stuttgart.de Abstract German has a productive morphology and allows the creation of complex words which are often highly ambiguous. This paper reports on the development of a head-lexicalized PCFG for the disam- biguation of German morphological anal- yses. The grammar is trained on unla- beled data using the Inside-Outside algo- rithm. The parser achieves a precision of more than 68% on difficult test data, which is 23% more than the baseline ob- tained by randomly choosing one of the simplest analyses. Remarkable is the fact that precision drops to 52% without lexi- calization. 1 Introduction German words may be as complex as the fol- lowing title of a bill: Rindfleischetikettierungs- ¨uberwachungsaufgaben¨ubertragungsgesetz (law for the transfer of the task of controlling the labeling of beef). The complexity is due to the productive morphological processes of derivation (e.g. Etiket- tierung (labeling) = Etikett (label) + ier (deriva- tional suffix) + ung (nominalization suffix)) and compounding (e.g. Rindfleisch (beef) = Rind (cattle) + Fleisch (meat)). For many words, there is more than one possible analysis. The German SMOR morphology (Schmid et al., 2004) e.g. generates 24 analyses for the word Abteilungen. If differences in the case feature are ignored, there are still six analy- ses, all of them plural: • Abt (abbot) Ei (egg) Lunge (lung) n (plural in- flectional ending) • Abt (abbot) ei (abbot → abbey) Lunge (lung) n (plural inflectional ending) • Abt (abbot) eil (hurry) ung (nominalization suf- fix) en (plural inflectional ending) • Abtei (abbey) Lunge (lung) n (plural inflec- tional ending) • Abteilung (department) en (plural inflectional ending) • ab (separable verb prefix) teil (divide) ung (nominalization suffix) en (plural inflectional ending) Here – and in many other cases, as well – the least complex analysis (defined as the number of deriva- tion and compounding steps), namely the plural of Abteilung (department), is the correct one. heuristic is not always successful, however.This The word Reisende e.g. is analyzed as the compound of Reis (rice) + Ende (end), and alternately as the nom- inalization of the present participle reisend (travel- ing). The latter one is correct although it requires two derivational steps (formation of the participle plus nominalization), while the former requires only one compounding step. The least complex analysis is not necessarily unique. One reason is, that German word forms are often ambiguous wrt. number, gender and case. The adjective kleine (small) e.g. receives 7 analyses by SMOR which differ only in the agreement features. 515 Another reason is that word forms are ambiguous wrt. the part of speech. The word gerecht e.g. is ei- ther an adjective (fair) or the past participle of the verb rechen (to rake). Similarly, the word gerade is either an adjective (straight) or an adverb (just). …

Content truncated to 500 words

Labeled Text

German has a productive morphology and allows the creation of complex words which are often highly ambiguous. This paper reports on the development of a head-lexicalized PCFG for the disambiguation of German morphological analyses. The grammar is trained on unlabeled data using the Inside-Outside algorithm. The parser achieves a precision of more than 68% on difficult test data, which is 23% more than the baseline obtained by randomly choosing one of the simplest analyses. Remarkable is the fact that precision drops to 52% without lexicalization. 1

Publication Details

Paper ID:
12656
Year:
2005
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan