In this paper , we study the problem of automatic enrichment of a morphologically underspecified treebank for Arabic , a morphologically rich language . We show that we can map from a tagset of size six to one with 485 tags at an accuracy rate of 94%-95 % . We can also identify the unspecified lemmas in the treebank with an accuracy over 97 % . Furthermore , we demonstrate that using our automatic annotations improves the performance of a state-of-the-art Arabic morphological tagger . Our approach combines a variety of techniques from corpus-based statistical models to linguistic rules that target specific phenomena . These results suggest that the cost of treebanking can be reduced by designing underspecified treebanks that can be subsequently enriched automatically . Collections of manually-annotated morphological and syntactic analyses of sentences , or treebanks , are an important resource for building statistical parsing models or for syntax-aware approaches to applications such as machine translation . Rich treebank annotations have also been used for a variety of natural language processing ( NLP ) applications such as tokenization , diacritization , part-of-speech ( POS ) tagging , morphological disambiguation , base phrase chunking , and semantic role labeling . The development of a treebank with rich annotations is demanding in time and money , especially for morphologically complex languages . Consequently , the richer the annotation , the slower the annotation process and the smaller the size of the treebank . As such , a tradeoff is usually made between the size of the treebank and the richness of its annotations . In this paper , we investigate the possibility of automatically enriching the morphologically underspecified Columbia Arabic Treebank ( CATiB ) ( Habash and Roth , 2009 ; Habash et al . , 2009 ) with the more complex POS tags and lemmas used in the Penn Arabic Treebank ( PATB ) ( Maamouri et al . , 2004 ) . We employ a variety of techniques that range from corpus-based statistical models to handwritten rules based on linguistic observations . Our best method reaches accuracy rates of 94%-95 % on full POS tag identification . We can also identify the unspecified lemmas in CATiB with an accuracy over 97 % . 37 % of our POS tag errors are due to gold tree or gold POS errors . A learning curve experiment to evaluate the dependence of our method on annotated data shows that while the quality of some components may reduce sharply with less data ( 12 % absolute reduction in accuracy when using 1 32 of the data or some 10 K annotated words ) , the overall effect is a lot smaller ( 2 % absolute drop ) . These results suggest that the cost of treebanking can be reduced by designing underspecified treebanks that can be subsequently enriched automatically . The rest of this paper is structured as follows : Section 2 presents related work ; Section 3 details various language background facts about Arabic and its treebanking ; Section 4 explains our approach ; and Section 5 presents and discusses our results . We have demonstrated that an underspecified version of an Arabic treebank can be fully specified for Arabic 's rich morphology automatically at an accuracy rate of 94%-95 % for POS tags and 97 % for lemmas . Our approach combines a variety of techniques from corpus-based statistical models ( which require some rich annotations ) to linguistic rules that target specific phenomena . Since the underspecified treebank is much faster to manually annotate than its fully specified version , these results suggest that the cost of treebanking can be reduced by designing underspecified treebanks that can be subsequently enriched automatically . In the future , we plan to extend the automatic enrichment effort to include more complex features such as empty nodes and semantic labels . We also plan to take the insights from this effort and apply them to treebanks of other languages . A small portion of a treebank that is fully annotated in rich format will of course be needed before we can apply these insights to other languages .

Challenge: Developing a treebank with rich annotations for morphologically rich languages is time-consuming and expensive leading to a trade-off between the size and the annotation richness.
Approach: They propose to automatically enrich the underspecified Arabic Treebank with complex POS tags and lemmas by employing techniques from corpus-based statistical models to rule-based methods.
Outcome: Their methods achieve high accuracy on full POS tag and the unspecified lemmas identification indicating the Treebank annotation cost can be reduced by automatic methods.