A Transition-based Algorithm for AMR Parsing

Abstract

We present a two-stage framework to parse a sentence into its Abstract Meaning Representation (AMR). We first use a dependency parser to generate a dependency tree for the sentence. In the second stage, we design a novel transition-based algorithm that transforms the dependency tree to an AMR graph. There are several advantages with this approach. First, the dependency parser can be trained on a training set much larger than the training set for the tree-to-graph algorithm, resulting in a more accurate AMR parser overall. Our parser yields an improvement of 5% absolute in F-measure over the best previous result. Second, the actions that we design are linguistically intuitive and capture the regularities in the mapping between the dependency structure and the AMR of a sentence. Third, our parser runs in nearly linear time in practice in spite of a worst-case complexity of O(n 2 ).

Broad-coverage CCG Semantic Parsing with AMR

“…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). Table 2 shows SMATCH scores for the developments set, with ablations.…”

“…AMRs, including graph parsing (Flanigan et al, 2014), methods to build AMRs from dependency trees (Wang et al, 2015) and algorithms for aligning words to AMRs (Pourdamghani et al, 2014).…”

A Synchronous Hyperedge Replacement Grammar based approach for AMR parsing

“…JAMR is the baseline result from Flanigan et al (2014). Wang et al (2015) shows the current state-ofart for string-to-AMR parsing. Without the dependency parse information and complex global features, our SHRG-based approach can already achieve competitive results in comparison with these two algorithms.…”

“…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. In the second step, a transition-based algorithm is used to greedily modify the dependency tree into an AMR graph.…”

CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks

“…(Wang et al, 2015;Vanderwende et al, 2015;Peng et al, 2015;Pust et al, 2015;Artzi et al, 2015;Flanigan et al, 2014;Werling et al, 2015). In contrast, we follow the spirit of minimal feature extraction using pre-trained word embeddings, as in (Collobert et al, 2011) and a recurrent network architecture similar to that described in (Zhou and Xu, 2015).…”