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.

Data-Driven Broad-Coverage Grammars for Opinionated Natural Language Generation (ONLG)

“…Cagan et al (2014) and Li et al (2017)). Our evaluation shows that while relational realizational (RR) inspired grammars (Tsarfaty and Sima'an, 2008) get good language model scores, simple head-driven lexicalized grammars a la Collins (2003) get better human rating and are more sensitive to sentiment. Furthermore, we show that incorporating topic models into the grammar-based generation makes the generated responses more relevant to the document content.…”

“…For coherent generation we would like to also control for the function of nodes in the derivation. To this end, we define a grammar and a generative story in the spirit of the RelationalRealizational (RR) grammar of Tsarfaty (2010).…”

Parsing Techniques using Paninian Framework on Nepali Language

“…The abbreviation for karaka is shown in table 1. Following the rules indicated for constraint graph in [2] and considering the direction of the age from right to left, the constraint graph and its solution graph can be obtained. The constraint graph for S.1 is shown in figure 2.…”

“…2. SOLUTION USING CONSTRAINT GRAPH As mentioned in [1], [2], the role of the core parser is to determine karaka relations and senses of words from the given local word clusters in a sentence.…”

“…Parsing is a process by which an input sentence is analysed and assigned a suitable structure [1][2][3]. Parsing process makes use of two components: a parser which is the procedural component (a computer program), and grammar which is declarative [4][5][6][7][8].…”