Showing papers on "Tree-adjoining grammar published in 2019"
01 Jan 2019
TL;DR: It is shown that Watson-Crick linear grammars can generate some context-sensitive languages and are not comparable with the family of linear languages.
Abstract: In this paper, we define Watson-Crick linear grammars extending Watson-Crick regular grammars Subramanian et al. (CCSEIT’12 proceedings of the second international conference on computer science, science, engineering and information technology 151–156, 2012, [9]) with linear rules, and study their generative power. We show that Watson-Crick linear grammars can generate some context-sensitive languages. Moreover, we establish that the family of Watson-Crick regular languages proper subset of the family of Watson-Crick linear languages but it is not comparable with the family of linear languages.
6 citations
01 Jun 2019
TL;DR: The achieved performance of the proposed method was comparable to that obtained by state-of-the-art non-linear system identification methods that do take advantage of correct selection of model structure and complexity based on a priori information.
Abstract: In this paper we propose a novel approach to identify dynamical systems. The method estimates the model structure and the parameters of the model simultaneously, automating the critical decisions involved in identification such as model structure and complexity selection. In order to solve the combined model structure and model parameter estimation problem, a new representation of dynamical systems is proposed. The proposed representation is based on Tree Adjoining Grammar, a formalism that was developed from linguistic considerations. Using the proposed representation, the identification problem can be interpreted as a multiobjective optimization problem and we propose an Evolutionary Algorithm-based approach to solve it. A benchmark example is used to demonstrate the proposed approach. The achieved performance of the proposed method, without making use of knowledge of the system description, was comparable to that obtained by state-of-the-art non-linear system identification methods that do take advantage of correct selection of model structure and complexity based on a priori information.
5 citations
Proceedings Article•
16 Dec 2019TL;DR: This article extended the approach described in Kasai et al. (2017, 2018) to n-best supertags and k-best dependency arcs and combine it with a subsequent A*-parsing step that extends the TAG parser from Waszczuk.
Abstract: In this paper, we extend recent approaches to Lexicalized Tree Adjoining Grammar (LTAG) parsing that combine supertagging with dependency parsing. In other words, we assign supertags (= unanchored elementary trees) to lexical items and we compute substitution/adjunction arcs between them. Kasai et al. (2017, 2018) jointly predict these structures with a neural graph-based parser. Predicting 1-best supertags and dependency arcs (as in Kasai et al. (2017, 2018)) however leads only to partial parsing due to incompatibilities between elementary trees and derivation trees. We therefore extend the approach described in Kasai et al. (2017, 2018) to n-best supertags and k-best dependency arcs and combine it with a subsequent A*-parsing step that extends the TAG parser from Waszczuk (2017). We show that this architecture allows for efficient full TAG parsing while being sufficiently accurate. We test our architecture on an LTAG extracted from the French Treebank (FTB).
3 citations
TL;DR: The empirical motivation for this expansion of generative semantics comes from the analysis of an apparently anomalous case of secondary predication in Spanish and English, which –the authors argue- shares properties with depictive and resultative constructions but in fact is neither.
2 citations
Patent•
19 Sep 2019
TL;DR: In this article, a matrix-vector representation of the tree-adjoining grammar (TAG) is used for parsing one token at a time, and a grammar parser reads in a sequence of tokens and determines if the sequence is valid in the grammar.
Abstract: A method and system for parsing a tree-adjoining grammar (TAG). A grammar parser reads in a sequence of tokens and determines if the sequence is valid in the grammar. It uses a matrix-vector representation of the TAG and performs parsing one token at a time. The innovation is the use of matrices and vectors to efficiently store the grammar and perform the parsing using these matrices and vectors.
Dissertation•
17 May 2019
TL;DR: It is shown how STAG can not only capture the syntactic distribution and semantic representation of both reflexives and reciprocals, but also do so in a unified way.
Abstract: An attractive feature of the formalism of synchronous tree adjoining grammar (STAG) is its potential to handle linguistic phenomena whose syntactic and semantic derivations seem to diverge. Recent work has aimed at adapting STAG to capture such cases. Anaphors, including both reflexives and reciprocals, have presented a particular challenge due to the locality constraints imposed by the STAG formalism. Previous attempts to model anaphors in STAG have focused specifically on reflexives and have not expanded to incorporate reciprocals. We show how STAG can not only capture the syntactic distribution and semantic representation of both reflexives and reciprocals, but also do so in a unified way.