Wichayaporn Wongkamjan, Feng Gu, Yanze Wang, Ulf Hermjakob, Jonathan May, Brandon M. Stewart, Jonathan K. Kummerfeld, Denis Peskoff, Jordan Lee Boyd-Graber. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

There are few corpora that endeavor to represent the semantic content of entire documents. We present a corpus that accomplishes one way of capturing document level semantics, by annotating coreference and similar phenomena (bridging and implicit roles) on top of gold Abstract Meaning Representations of sentence-level semantics. We present a new corpus of this annotation, with analysis of its quality, alongside a plausible baseline for comparison. It is hoped that this Multi-Sentence AMR corpus (MS-AMR) may become a feasible method for developing rich representations of document meaning, useful for tasks such as information extraction and question answering.

We advance the state of the art in biomolecular interaction extraction with three contributions: (i) We show that deep, Abstract Meaning Representations (AMR) significantly improve the accuracy of a biomolecular interaction extraction system when compared to a baseline that relies solely on surface- and syntax-based features; (ii) In contrast with previous approaches that infer relations on a sentence-by-sentence basis, we expand our framework to enable consistent predictions over sets of sentences (documents); (iii) We further modify and expand a graph kernel learning framework to enable concurrent exploitation of automatically induced AMR (semantic) and dependency structure (syntactic) representations. Our experiments show that our approach yields interaction extraction systems that are more robust in environments where there is a significant mismatch between training and test conditions.

We present a method for generating English sentences from Abstract Meaning Representation (AMR) graphs, exploiting a parallel corpus of AMRs and English sentences.We treat AMR-to-English generation as phrase-based machine translation (PBMT).We introduce a method that learns to linearize tokens of AMR graphs into an English-like order.Our linearization reduces the amount of distortion in PBMT and increases generation quality.We report a Bleu score of 26.8 on the standard AMR/English test set.

We present a parser for Abstract Meaning Representation (AMR).We treat Englishto-AMR conversion within the framework of string-to-tree, syntax-based machine translation (SBMT).To make this work, we transform the AMR structure into a form suitable for the mechanics of SBMT and useful for modeling.We introduce an AMR-specific language model and add data and features drawn from semantic resources.Our resulting AMR parser significantly improves upon state-of-the-art results.

Xiaoman Pan, Taylor Cassidy, Ulf Hermjakob, Heng Ji, Kevin Knight. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2015.

We align pairs of English sentences and corresponding Abstract Meaning Repre-sentations (AMR), at the token level. Such alignments will be useful for downstream extraction of semantic interpretation and generation rules. Our method involves linearizing AMR structures and perform-ing symmetrized EM training. We obtain 86.5 % and 83.1 % alignment F score on de-velopment and test sets. 1

We present a knowledge and context-based system for parsing and translating natural language and evaluate it on sentences from the Wall Street Journal. Applying machine learning techniques, the system uses parse action examples acquired under supervision to generate a deterministic shift-reduce parser in the form of a decision structure. It relies heavily on context, as encoded in features which describe the morphological, syntactic, semantic and other aspects of a given parse state.