21,693 research outputs found
Robust Subgraph Generation Improves Abstract Meaning Representation Parsing
The Abstract Meaning Representation (AMR) is a representation for open-domain
rich semantics, with potential use in fields like event extraction and machine
translation. Node generation, typically done using a simple dictionary lookup,
is currently an important limiting factor in AMR parsing. We propose a small
set of actions that derive AMR subgraphs by transformations on spans of text,
which allows for more robust learning of this stage. Our set of construction
actions generalize better than the previous approach, and can be learned with a
simple classifier. We improve on the previous state-of-the-art result for AMR
parsing, boosting end-to-end performance by 3 F on both the LDC2013E117 and
LDC2014T12 datasets.Comment: To appear in ACL 201
Learning the Semantics of Manipulation Action
In this paper we present a formal computational framework for modeling
manipulation actions. The introduced formalism leads to semantics of
manipulation action and has applications to both observing and understanding
human manipulation actions as well as executing them with a robotic mechanism
(e.g. a humanoid robot). It is based on a Combinatory Categorial Grammar. The
goal of the introduced framework is to: (1) represent manipulation actions with
both syntax and semantic parts, where the semantic part employs
-calculus; (2) enable a probabilistic semantic parsing schema to learn
the -calculus representation of manipulation action from an annotated
action corpus of videos; (3) use (1) and (2) to develop a system that visually
observes manipulation actions and understands their meaning while it can reason
beyond observations using propositional logic and axiom schemata. The
experiments conducted on a public available large manipulation action dataset
validate the theoretical framework and our implementation
TRX: A Formally Verified Parser Interpreter
Parsing is an important problem in computer science and yet surprisingly
little attention has been devoted to its formal verification. In this paper, we
present TRX: a parser interpreter formally developed in the proof assistant
Coq, capable of producing formally correct parsers. We are using parsing
expression grammars (PEGs), a formalism essentially representing recursive
descent parsing, which we consider an attractive alternative to context-free
grammars (CFGs). From this formalization we can extract a parser for an
arbitrary PEG grammar with the warranty of total correctness, i.e., the
resulting parser is terminating and correct with respect to its grammar and the
semantics of PEGs; both properties formally proven in Coq.Comment: 26 pages, LMC
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