2,123 research outputs found
Non-self-embedding linear context-free tree grammars generate regular tree languages
For the class of linear context-free tree grammars, we define a decidable property called self-embedding. We prove that each non-self-embedding grammar in this class generates a regular tree language and show how to construct the equivalent regular tree grammar.PostprintPeer reviewe
Graphical Reasoning in Compact Closed Categories for Quantum Computation
Compact closed categories provide a foundational formalism for a variety of
important domains, including quantum computation. These categories have a
natural visualisation as a form of graphs. We present a formalism for
equational reasoning about such graphs and develop this into a generic proof
system with a fixed logical kernel for equational reasoning about compact
closed categories. Automating this reasoning process is motivated by the slow
and error prone nature of manual graph manipulation. A salient feature of our
system is that it provides a formal and declarative account of derived results
that can include `ellipses'-style notation. We illustrate the framework by
instantiating it for a graphical language of quantum computation and show how
this can be used to perform symbolic computation.Comment: 21 pages, 9 figures. This is the journal version of the paper
published at AIS
Geometric representations for minimalist grammars
We reformulate minimalist grammars as partial functions on term algebras for
strings and trees. Using filler/role bindings and tensor product
representations, we construct homomorphisms for these data structures into
geometric vector spaces. We prove that the structure-building functions as well
as simple processors for minimalist languages can be realized by piecewise
linear operators in representation space. We also propose harmony, i.e. the
distance of an intermediate processing step from the final well-formed state in
representation space, as a measure of processing complexity. Finally, we
illustrate our findings by means of two particular arithmetic and fractal
representations.Comment: 43 pages, 4 figure
Punctuation in Quoted Speech
Quoted speech is often set off by punctuation marks, in particular quotation
marks. Thus, it might seem that the quotation marks would be extremely useful
in identifying these structures in texts. Unfortunately, the situation is not
quite so clear. In this work, I will argue that quotation marks are not
adequate for either identifying or constraining the syntax of quoted speech.
More useful information comes from the presence of a quoting verb, which is
either a verb of saying or a punctual verb, and the presence of other
punctuation marks, usually commas. Using a lexicalized grammar, we can license
most quoting clauses as text adjuncts. A distinction will be made not between
direct and indirect quoted speech, but rather between adjunct and non-adjunct
quoting clauses.Comment: 11 pages, 11 ps figures, Proceedings of SIGPARSE 96 - Punctuation in
Computational Linguistic
Graph-to-Sequence Learning using Gated Graph Neural Networks
Many NLP applications can be framed as a graph-to-sequence learning problem.
Previous work proposing neural architectures on this setting obtained promising
results compared to grammar-based approaches but still rely on linearisation
heuristics and/or standard recurrent networks to achieve the best performance.
In this work, we propose a new model that encodes the full structural
information contained in the graph. Our architecture couples the recently
proposed Gated Graph Neural Networks with an input transformation that allows
nodes and edges to have their own hidden representations, while tackling the
parameter explosion problem present in previous work. Experimental results show
that our model outperforms strong baselines in generation from AMR graphs and
syntax-based neural machine translation.Comment: ACL 201
Deepr: A Convolutional Net for Medical Records
Feature engineering remains a major bottleneck when creating predictive
systems from electronic medical records. At present, an important missing
element is detecting predictive regular clinical motifs from irregular episodic
records. We present Deepr (short for Deep record), a new end-to-end deep
learning system that learns to extract features from medical records and
predicts future risk automatically. Deepr transforms a record into a sequence
of discrete elements separated by coded time gaps and hospital transfers. On
top of the sequence is a convolutional neural net that detects and combines
predictive local clinical motifs to stratify the risk. Deepr permits
transparent inspection and visualization of its inner working. We validate
Deepr on hospital data to predict unplanned readmission after discharge. Deepr
achieves superior accuracy compared to traditional techniques, detects
meaningful clinical motifs, and uncovers the underlying structure of the
disease and intervention space
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