A Discriminative Graph-Based Parser for the Abstract Meaning Representation

Abstract

Meaning Representation (AMR) is a semantic formalism for which a growing set of annotated examples is available. We introduce the first approach to parse sentences into this representation, providing a strong baseline for future improvement. The method is based on a novel algorithm for finding a maximum spanning, connected subgraph, embedded within a Lagrangian relaxation of an optimization problem that imposes linguistically inspired constraints. Our approach is described in the general framework of structured prediction, allowing future incorporation of additional features and constraints, and may extend to other formalisms as well. Our open-source system, JAMR, is available at:http://github.com/jflanigan/jamr

Broad-coverage CCG Semantic Parsing with AMR

“…Our approach outperforms JAMR by 3 SMATCH F1 points, with a significant gain in recall. Given consensus inter-annotator agreement of 83 SMATCH F1 (Flanigan et al, 2014), this improvement reduces the gap between automated methods and human performance by 15%. Although not strictly comparable, Table 1 also includes results on the pre-release AMR Bank corpus, including the published JAMR results, their fixed results and the results of Wang et al (2015).…”

ICL-HD at SemEval-2016 Task 8: Meaning Representation Parsing - Augmenting AMR Parsing with a Preposition Semantic Role Labeling Neural Network

“…They achieve an F-score of 0.58 on the LDC2013E117 corpus. Werling, Angeli, and Manning (2015) improve the AMR parsing concept of Flanigan et al (2014) by supporting the critical task of concept identification with a predefined set of actions for concept subgraph generation that are evoked after a statistical classification procedure. Besides graph-based approaches, there exist also other strategies on AMR parsing: Peng, Song, and Gildea (2015) learn synchronous hyperedge replacement grammar rules from string- graph pairs.…”

“…It converts dependency trees into AMR graphs with a transition-based technique by evoking certain tree transforming actions at reached transition states. In the training procedure, the tokens of the input sentence are first aligned with the nodes of its gold AMR graph using the JAMR aligner (Flanigan et al 2014). Such aligned AMR graphs are represented as span graphs storing token spans for AMR concept nodes.…”

A Synchronous Hyperedge Replacement Grammar based approach for AMR parsing

“…The task of AMR graph parsing is to map natural language strings to AMR semantic graphs. Flanigan et al (2014) propose a two-stage parsing algorithm which first maps meaningful continuous spans on the string side to concept fragments on the graph side, and then in the second stage adds additional edges to make all these fragments connected. Concept identification (Flanigan et al, 2014;Pourdamghani et al, 2014) can be considered as an important first step to relate components of the string to components in the graph.…”

“…Flanigan et al (2014) propose a two-stage parsing algorithm which first maps meaningful continuous spans on the string side to concept fragments on the graph side, and then in the second stage adds additional edges to make all these fragments connected. Concept identification (Flanigan et al, 2014;Pourdamghani et al, 2014) can be considered as an important first step to relate components of the string to components in the graph. Wang et al (2015) also present a two-stage procedure where they first use a dependency parser trained on a large corpus to generate a dependency tree for each sentence.…”