WISeN: Widely Interpretable Semantic Network for Richer Meaning Representation 公开

Feng, Lydia (Spring 2021)

Permanent URL: https://etd.library.emory.edu/concern/etds/qz20st721?locale=zh
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Abstract

Many semantic annotations currently utilize Abstract Meaning Representation and PropBank frameset files to represent meaning. This scheme relies on arbitrary predicate-argument structures comprising unintuitive numbered arguments, fine-grained sense-disambiguation, and high start-up costs. To address these issues, we present a new annotation scheme, WISeN, that prioritizes semantic roles over numbered arguments and does away with sense-disambiguation. This scheme aims to be more intuitive for annotators and more interpretable by parsers. We evaluate this annotation scheme with a two-part experiment. First, we measure speed and accuracy of manual annotations. Second, we train a parser on both AMR and WISeN annotations and measure model accuracy. The results show that WISeN supports improved parser performance and increased inter-annotator agreement without sacrificing annotation speed compared to AMR. As such, we advocate for the adoption of WISeN as an annotation scheme for semantic representations.

Table of Contents

1 Introduction ................................................................ 1

2 Background, Related Work, & Rationale ......................... 4

2.1 Semantic Representations of Language ....................... 4

2.2 Thematic Roles ......................................................... 6

2.2.1 Semantics of Numbered Arguments ......................... 6

2.2.2 VerbNet Thematic Roles ......................................... 10

2.3 Sense Disambiguation .............................................. 14

2.4 Abstract Meaning Representation .............................. 15

2.5 AMR Parsing ............................................................ 17

2.6 Rationale ................................................................. 18

3 Methodology .............................................................. 23

3.1 Overview ................................................................. 23

3.2 Annotation Experiment ............................................ 25

3.2.1 Corpus .................................................................. 25

3.2.2 Annotators ........................................................... 26

3.2.3 Procedure ............................................................. 27

3.2.4 Evaluation Metrics ................................................ 28

3.3 Parsing Experiment ................................................. 28

3.3.1 AMR Corpus ......................................................... 29

3.3.2 WISeN Corpus ...................................................... 30

3.3.3 Parsing AMR and WISeN ....................................... 36

3.3.4 Evaluation Metrics ................................................ 39

4 Results ...................................................................... 41

4.1 Annotation Experiment .......................................... 41

4.1.1 Inter-Annotator Agreement ................................... 41

4.1.2 Speed of Annotations ........................................... 44

4.2 Parsing Experiment ................................................ 45

5 Discussion ................................................................ 49

5.1 WISeN Annotation is More Accurate than AMR ......... 49

5.2 WISeN Annotation is Comparable in Speed to AMR .. 50

5.3 WISeN Improves Parser Performance ....................... 51

5.4 Limitations ............................................................ 52

5.5 Future Work .......................................................... 55

5.6 Conclusions .......................................................... 56

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