Relational-Realizational Parsing

Abstract State-of-the-art statistical parsing models applied to free word-order languages tend to underperform compared to, e.g., parsing English. Constituency-based models often fail to capture generalizations that cannot be stated in structural terms, and dependency-based models employ a 'single-head' assumption that often breaks in the face of multiple exponence. In this paper we suggest that the position of a constituent is a form manifestation of its grammatical function, one among various possible means of realization. We develop the Relational-Realizational approach to parsing in which we untangle the projection of grammatical functions and their means of realization to allow for phrase-structure variability and morphological-syntactic interaction. We empirically demonstrate the application of our approach to parsing Modern Hebrew, obtaining 7% error reduction from previously reported results

Statistical parsing of morphologically rich languages (SPMRL): what, how and whither

The term Morphologically Rich Languages (MRLs) refers to languages in which significant information concerning syntactic units and relations is expressed at word-level. There is ample evidence that the application of readily available statistical parsing models to such languages is susceptible to serious performance degradation. The first workshop on statistical parsing of MRLs hosts a variety of contributions which show that despite language-specific idiosyncrasies, the problems associated with parsing MRLs cut across languages and parsing frameworks. In this paper we review the current state-of-affairs with respect to parsing MRLs and point out central challenges. We synthesize the contributions of researchers working on parsing Arabic, Basque, French, German, Hebrew, Hindi and Korean to point out shared solutions across languages. The overarching analysis suggests itself as a source of directions for future investigations

Relating Morphology to Syntax

B1 - Research Book Chapter

Morphology Within the Parallel Architecture Framework : the Centrality of the Lexicon Below the Word Level

The Parallel Architecture (PA) framework (Jackendoff 2002, 2007, Culicover & Jackendoff 2005) is one of the most complete constraint-based linguistic theories that encompasses phonology, syntax and semantics. However, it lacks a fully developed model of word formation. More recently, a theory called Relational Morphology (RM) (Jackendoff & Audring 2020) has been developed, that integrates into the PA. The current study shows how the Slot Structure model (Benavides 2003, 2009, 2010), which is compatible with the PA and is based on the dual-route model and percolation of features (Pinker 1999, 2006; Huang & Pinker 2010), can provide a better account of morphology than RM, and can also be incorporated into the PA, thus contributing to make this a more explanatory framework. Spanish data are used as the basis to demonstrate the implementation of the SSM. The current paper demonstrates two key problems for RM: inconsistent and confusing coindexation, and a proliferation of schemas, and shows that these issues do not arise in the Slot Structure model. Overall, the paper points out significant drawbacks in the RM framework, while at the same time showing how the PA's morphological component can be enriched with the Slot Structure model

Hard constraints for grammatical function labelling

For languages with (semi-) free word order (such as German), labelling grammatical functions on top of phrase-structural constituent analyses is crucial for making them interpretable. Unfortunately, most statistical classifiers consider only local information for function labelling and fail to capture important restrictions on the distribution of core argument functions such as subject, object etc., namely that there is at most one subject (etc.) per clause. We augment a statistical classifier with an integer linear program imposing hard linguistic constraints on the solution space output by the classifier, capturing global distributional restrictions. We show that this improves labelling quality, in particular for argument grammatical functions, in an intrinsic evaluation, and, importantly, grammar coverage for treebankbased (Lexical-Functional) grammar acquisition and parsing, in an extrinsic evaluation

Registerial cartography: context-based mapping of text types and their rhetorical-relational organization

Department of EnglishInvited conference pape

GeLexi project : sentence parsing based on a GEnerative LEXIcon

The principal aim of our research team, called GeLexi, is to legitimate a new sort of generative grammar via verifying its computational implementability. This grammar is more radically "lexicalist" than any earlier one: no phrase structure trees are generated, but word order is accounted for by means of ranked parameters. Another novelty is the extension of "total lexicalism" to morphology: lexical items are assigned not to words but to morphemes. Our parser, in accordance with the basic task of every generative grammar, decides whether a sentence is grammatical, and if it is, then provides a morphophonological analysis, a compilation of grammatical relations, and two kinds of semantic representations. At the end we show some examples to demonstrate our procedures, among them a sentence containing the conjunction és 'and', which is our latest development