Papers by Michael Wayne Goodman
Penman: An Open-Source Library and Tool for AMR Graphs (2020.acl-demos)
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| Challenge: | Abstract Meaning Representation encodes acyclic graphs in PENMAN notation format . the open-source Python library Penman provides a robust parser and functions for graph inspection and manipulation . |
| Approach: | They propose a framework for encoding acyclic graphs in PENMAN notation . the open-source Python library Penman provides a robust parser and functions for graph inspection and manipulation . |
| Outcome: | The open-source Python library Penman provides a robust parser, functions for graph inspection and manipulation, and functions for formatting graphs into PENMAN notation. |
PDF-to-Text Reanalysis for Linguistic Data Mining (L18-1)
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| Challenge: | In the 1990s, extracting semistructured text from scientific writing in PDF files was largely a computer vision and OCR problem. |
| Approach: | They propose a system for the reanalysis of PDF-extracted text that performs block detection, respacing, and tabular data analysis for linguistic data mining. |
| Outcome: | The proposed system eliminates the extreme verbosity of XML output while leaving important positional information available for downstream processes. |
Some Issues with Building a Multilingual Wordnet (2020.lrec-1)
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| Challenge: | Notable extensions include: confidence, corpus frequency, orthographic variants, lexicalized and non-lexicalised synsets and lemmas, new parts of speech, and more. |
| Approach: | They propose to integrate a new open multilingual wordnet format that tests the extensions introduced by the new format and integrates a set of tools to ensure the integrity of the Collaborative Interlingual Index. |
| Outcome: | The proposed format integrates a set of tools that test the extensions while ensuring the integrity of the Collaborative Interlingual Index (CILI). |
Neural Text Generation from Rich Semantic Representations (N19-1)
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| Challenge: | 2 is a neural model that maps a linearization of Dependency MRS to text . 1 is based on a BLEU score of 66.11 when trained on gold data . |
| Approach: | They propose to use Minimal Recursion Semantics to generate high-quality text from structured representations. |
| Outcome: | The proposed model achieves a BLEU score of 77.17 on the full test set and 83.37 on the subset of test data most closely matching the silver data domain. |