666,862 research outputs found
Evaluating Scoped Meaning Representations
Semantic parsing offers many opportunities to improve natural language
understanding. We present a semantically annotated parallel corpus for English,
German, Italian, and Dutch where sentences are aligned with scoped meaning
representations in order to capture the semantics of negation, modals,
quantification, and presupposition triggers. The semantic formalism is based on
Discourse Representation Theory, but concepts are represented by WordNet
synsets and thematic roles by VerbNet relations. Translating scoped meaning
representations to sets of clauses enables us to compare them for the purpose
of semantic parser evaluation and checking translations. This is done by
computing precision and recall on matching clauses, in a similar way as is done
for Abstract Meaning Representations. We show that our matching tool for
evaluating scoped meaning representations is both accurate and efficient.
Applying this matching tool to three baseline semantic parsers yields F-scores
between 43% and 54%. A pilot study is performed to automatically find changes
in meaning by comparing meaning representations of translations. This
comparison turns out to be an additional way of (i) finding annotation mistakes
and (ii) finding instances where our semantic analysis needs to be improved.Comment: Camera-ready for LREC 201
The Epistemological Foundations of Knowledge Representations
This paper looks at the epistemological foundations of knowledge
representations embodied in retrieval languages. It considers questions
such as the validity of knowledge representations and their effectiveness
for the purposes of retrieval and automation. The knowledge
representations it considers are derived from three theories of meaning that
have dominated twentieth-century philosophy.published or submitted for publicatio
AMR Dependency Parsing with a Typed Semantic Algebra
We present a semantic parser for Abstract Meaning Representations which
learns to parse strings into tree representations of the compositional
structure of an AMR graph. This allows us to use standard neural techniques for
supertagging and dependency tree parsing, constrained by a linguistically
principled type system. We present two approximative decoding algorithms, which
achieve state-of-the-art accuracy and outperform strong baselines.Comment: This paper will be presented at ACL 2018 (see
https://acl2018.org/programme/papers/
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