An Incremental Parser for Abstract Meaning Representation (1608.06111v5)

Abstract: Meaning Representation (AMR) is a semantic representation for natural language that embeds annotations related to traditional tasks such as named entity recognition, semantic role labeling, word sense disambiguation and co-reference resolution. We describe a transition-based parser for AMR that parses sentences left-to-right, in linear time. We further propose a test-suite that assesses specific subtasks that are helpful in comparing AMR parsers, and show that our parser is competitive with the state of the art on the LDC2015E86 dataset and that it outperforms state-of-the-art parsers for recovering named entities and handling polarity.

• The paper introduces a novel transition-based parser that incrementally processes Abstract Meaning Representation (AMR) in linear time.
• It adapts dependency parsing strategies to handle complex AMR structures like non-projectivity and reentrancy.
• The parser demonstrates competitive performance, particularly excelling in specific subtasks like named entity recognition, emphasizing efficient semantic processing.

Overview of Incremental AMR Parsing

The paper "An Incremental Parser for Abstract Meaning Representation" presented by Damonte et al. introduces a transition-based parser for Abstract Meaning Representation (AMR), emphasizing enhancements in parsing efficiency for semantic representations of natural language. The authors detail the development of a parser that processes sentences incrementally from left-to-right in linear time, highlighting its competitive performance relative to existing state-of-the-art AMR parsers.

AMR Parsing and Transition-Based Approach

AMR serves as a comprehensive framework for semantic representation, encompassing shallow-semantic tasks such as named entity recognition (NER), semantic role labeling, and word sense disambiguation within a unified dataset. The paper outlines the motivations for developing parsers capable of efficiently recovering AMR structures without sacrificing accuracy. The proposed parser takes inspiration from dependency transition systems, notably those by Nivre, adapting such methods to address the unique challenges posed by AMR structures, including non-projectivity and reentrancy.

Key Features and Innovations

• Transition System Design: The paper introduces a transition system tailored for AMR parsing. This system leverages greedy transition-based approaches, optimizing parsing operations to achieve linear time complexity—a significant advantage for real-time applications and incremental semantic interpretation.
• Adaptations for AMR Structures: The parser incorporates algorithmic adaptations to address non-projective edges and reentrant nodes, both crucial for AMR graphs which often diverge from traditional dependency tree assumptions.
• Fine-Grained Evaluation: Recognizing the limitations of the Smatch score as an evaluation metric, the authors propose a multi-metric evaluation suite. This suite disaggregates parsing performance across various subtasks including unnamed edges, concept identification, named entity detection, and semantic role labeling. Such a granular evaluation facilitates a more nuanced understanding of parser strengths and weaknesses across different dimensions of the AMR parsing challenge.

Experimental Outcomes

The parser exhibits competitive performance, particularly excelling in tasks such as unlabeled edge recovery, concept identification, and named entity recognition. Although the parser does not claim superior results in the holistic Smatch score, its strengths in specific subtasks suggest practical utility in domains where certain NLP tasks are prioritized, such as named entity extraction and negation handling. The authors underscore the parser's linear time complexity as conducive to efficient processing, marking a favorable compromise between parsing speed and semantic accuracy.

Implications and Future Directions

This research contributes a methodological advance in the field of semantic parsing, bridging dependency parsing strategies with AMR's requirements. It presents implications for both theoretical understanding of AMR as akin to dependency parsing and practical improvements in computational efficiency. Future work may explore further adaptations in transition systems, potentially improving edge labeling precision—identified as a challenge—and extending hooks for more comprehensive concept identification across wider linguistic phenomena.

In conclusion, the paper offers a substantive contribution to AMR parsing technology, emphasizing efficiency without neglecting the intricacies of semantic representation. The exploration of AMR structures through transition-based parsing establishes a promising avenue for advancing NLP applications wherein semantic richness and computational feasibility must be balanced effectively.