A Discriminative Model for Joint Morphological Disambiguation and Dependency Parsing

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 885–894, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics A Discriminative Model for Joint Morphological Disambiguation and Dependency Parsing John Lee Department of Chinese, Translation and Linguistics City University of Hong Kong jsylee@cityu.edu.hk Jason Naradowsky, David A. Smith Department of Computer Science University of Massachusetts, Amherst {narad,dasmith}@cs.umass.edu Abstract Most previous studies of morphological dis- ambiguation and dependency parsing have been pursued independently. Morphological taggers operate on n-grams and do not take into account syntactic relations; parsers use the “pipeline” approach, assuming that mor- phological information has been separately obtained. However, in morphologically-rich languages, there is often considerable interaction between morphology and syntax, such that neither can be disambiguated without the other. In this pa- per, we propose a discriminative model that jointly infers morphological properties and syntactic structures. In evaluations on various highly-inflected languages, this joint model outperforms both a baseline tagger in morpho- logical disambiguation, and a pipeline parser in head selection. 1 Introduction To date, studies of morphological analysis and dependency parsing have been pursued more or less independently. Morphological taggers dis- ambiguate morphological attributes such as part- of-speech (POS) or case, without taking syntax into account (Hakkani-T¨ur et al., 2000; Hajiˇc et al., 2001); dependency parsers commonly assume the “pipeline” approach, relying on morphologi- cal information as part of the input (Buchholz and Marsi, 2006; Nivre et al., 2007). This approach serves many languages well, especially those with less morphological ambiguity. In English, for ex- ample, accuracy of POS tagging has risen above 97% (Toutanova et al., 2003), and that of depen- dency parsing has reached the low nineties (Nivre et al., 2007). For these languages, there may be little to be gained to justify the computational cost of in- corporating syntactic inference during the morpho- logical tagging task; conversely, it is doubtful that errorful morphological information is a main cause of errors in English dependency parsing. However, the pipeline approach seems more prob- lematic for morphologically-rich languages with substantial interactions between morphology and syntax (Tsarfaty, 2006). Consider the Latin sen- tence, Una dies omnis potuit praecurrere amantis, ‘One day was able to make up for all the lovers’1. As shown in Table 1, the adjective omnis (‘all’) is am- biguous in number, gender, and case; there are seven valid analyses. From the perspective of a finite- state morphological tagger, the most attractive anal- ysis is arguably the singular nominative, since omnis is immediately followed by the singular verb potuit (‘could’). Indeed, the baseline tagger used in this study did make this decision. Given its nominative case, the pipeline parser assigned the verb potuit to be its head; the two words form the typical subject- verb relation, agreeing in number. Unfortunately, as shown in Figure 1, the word om- nis in fact modifies the noun amantis, at the end of the sentence. As a result, despite the distance be- tween them, they must agree in number, gender and case, i.e., both must …

Content truncated to 500 words