Linguistic Expressions and Semantic Processing

Butler, Alastair

doi:10.1007/978-3-319-18830-0

Cited by 12 publications

(5 citation statements)

References 3 publications

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“…One of these is the principle explicitly noted in the annotation manual that the annotation aims to achieve descriptive adequacy and maximum convenience in the retrieval of information. The other, less obvious factor is the fact that the NPCMJ corpus (or, more precisely, the Keyaki treebank that it is based on) was originally designed to serve as a learning model for the syntactic component of an integrated system of syntactic/semantic parser (Butler, 2015).…”

Section: Some Further Methodological Issuesmentioning

confidence: 99%

Probing the nature of an island constraint with a parsed corpus: A case study on the Coordinate Structure Constraint in Japanese

Kubota

Kubota²

2019

LILT

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This paper presents a case study of the use of the NINJAL Parsed Corpus of Modern Japanese (NPCMJ) for syntactic research. NPCMJ is the first phrase structure-based treebank for Japanese that is specifically designed for application in linguistic (in addition to NLP) research. After discussing some basic methodological issues pertaining to the use of treebanks for theoretical linguistics research, we introduce our case study on the status of the Coordinate Structure Constraint (CSC) in Japanese, showing that NPCMJ enables us to easily retrieve examples that support one of the key claims of Kubota and Lee (2015): that the CSC should be viewed as a pragmatic, rather than a syntactic constraint. The corpus-based study we conducted moreover revealed a previously unnoticed tendency that was highly relevant for further clarifying the principles governing the empirical data in question. We conclude the paper by briefly discussing some further methodological issues brought up by our case study pertaining to the relationship between linguistic research and corpus development.

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Section: Some Further Methodological Issuesmentioning

confidence: 99%

Probing the nature of an island constraint with a parsed corpus: A case study on the Coordinate Structure Constraint in Japanese

Kubota

Kubota²

2019

LILT

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“…The full list of operations and details are given in Butler (2015). Operations access or possibly alter a sequence based information state (Vermeulen, 2000) that retains binding information by assigning (possibly empty) sequences of values to binding names.…”

Section: Obtaining Semantic Analysismentioning

confidence: 99%

“…Having syntactic structures, the next step is to reach a level of semantic analysis. This is derived by printing off information accumulated with an adapted Tarskian satisfaction relation for a Dynamic Semantics (Dekker, 2012 The full list of operations and details are given in Butler (2015). Operations access or possibly alter a sequence based information state (Vermeulen, 2000) that retains binding information by assigning (possibly empty) sequences of values to binding names.…”

Section: Obtaining Semantic Analysismentioning

confidence: 99%

DynamicPower at SemEval-2016 Task 8: Processing syntactic parse trees with a Dynamic Semantics core

Butler

2016

Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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This is a system description paper for a submission to Task 8 of SemEval-2016: Meaning Representation Parsing. No use was made of the training data provided by the task. Instead existing components were combined to form a pipeline able to take raw sentences as input and output meaning representations. Components are a part-of-speech tagger and parser trained on the Penn Parsed Corpus of Modern British English to produce syntactic parse trees, a semantic role labeller and a named entity recogniser to supplement obtained parse trees with word sense, functional and named entity information, followed by an adapted Tarskian satisfaction relation for a Dynamic Semantics that is used to transform a syntactic parse into a predicate logic based meaning representation, followed by conversion to penman/AMR notation required for the task appraisal.

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“…The approach of this paper first requires a way to reach meaning representations from natural language input. Here, use is made of Treebank Semantics (Butler andYoshimoto 2012, Butler 2015), for the ease with which it fits into the described pipeline, since it takes as input the parsed trees that will be generated as output, and for the quality of meaning representations produced. Treebank Semantics works by converting parsed constituent tree annotations into expressions of a Dynamic Semantics language (Scope Control Theory or SCT; Butler 2015) which is processed against a sequence based information state (cf.…”

Section: Reaching Meaning Representationsmentioning

confidence: 99%

Round trips with meaning stopovers

Butler¹

2015

Proceedings of the 1st Workshop on Semantics-Driven Statistical Machine Translation (S2MT 2015)

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This paper describes taking parsed sentences, going to meaning representations (the stopover), and then back to parsed sentences (the round trip). Keeping to the same language tests the combined success of building meaning representations from parsed input and of generating parsed output. Switching languages when manipulating meaning representations would achieve translation. Transfer shortfall is seen with meaning representations built from parsed parallel corpora data, with English-Japanese as an example.

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Linguistic Expressions and Semantic Processing

Cited by 12 publications

References 3 publications

Probing the nature of an island constraint with a parsed corpus: A case study on the Coordinate Structure Constraint in Japanese

Probing the nature of an island constraint with a parsed corpus: A case study on the Coordinate Structure Constraint in Japanese

DynamicPower at SemEval-2016 Task 8: Processing syntactic parse trees with a Dynamic Semantics core

Round trips with meaning stopovers

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