22,127 research outputs found
Linear Logic for Meaning Assembly
Semantic theories of natural language associate meanings with utterances by
providing meanings for lexical items and rules for determining the meaning of
larger units given the meanings of their parts. Meanings are often assumed to
combine via function application, which works well when constituent structure
trees are used to guide semantic composition. However, we believe that the
functional structure of Lexical-Functional Grammar is best used to provide the
syntactic information necessary for constraining derivations of meaning in a
cross-linguistically uniform format. It has been difficult, however, to
reconcile this approach with the combination of meanings by function
application. In contrast to compositional approaches, we present a deductive
approach to assembling meanings, based on reasoning with constraints, which
meshes well with the unordered nature of information in the functional
structure. Our use of linear logic as a `glue' for assembling meanings allows
for a coherent treatment of the LFG requirements of completeness and coherence
as well as of modification and quantification.Comment: 19 pages, uses lingmacros.sty, fullname.sty, tree-dvips.sty,
latexsym.sty, requires the new version of Late
Semantic A-translation and Super-consistency entail Classical Cut Elimination
We show that if a theory R defined by a rewrite system is super-consistent,
the classical sequent calculus modulo R enjoys the cut elimination property,
which was an open question. For such theories it was already known that proofs
strongly normalize in natural deduction modulo R, and that cut elimination
holds in the intuitionistic sequent calculus modulo R. We first define a
syntactic and a semantic version of Friedman's A-translation, showing that it
preserves the structure of pseudo-Heyting algebra, our semantic framework. Then
we relate the interpretation of a theory in the A-translated algebra and its
A-translation in the original algebra. This allows to show the stability of the
super-consistency criterion and the cut elimination theorem
Joint Video and Text Parsing for Understanding Events and Answering Queries
We propose a framework for parsing video and text jointly for understanding
events and answering user queries. Our framework produces a parse graph that
represents the compositional structures of spatial information (objects and
scenes), temporal information (actions and events) and causal information
(causalities between events and fluents) in the video and text. The knowledge
representation of our framework is based on a spatial-temporal-causal And-Or
graph (S/T/C-AOG), which jointly models possible hierarchical compositions of
objects, scenes and events as well as their interactions and mutual contexts,
and specifies the prior probabilistic distribution of the parse graphs. We
present a probabilistic generative model for joint parsing that captures the
relations between the input video/text, their corresponding parse graphs and
the joint parse graph. Based on the probabilistic model, we propose a joint
parsing system consisting of three modules: video parsing, text parsing and
joint inference. Video parsing and text parsing produce two parse graphs from
the input video and text respectively. The joint inference module produces a
joint parse graph by performing matching, deduction and revision on the video
and text parse graphs. The proposed framework has the following objectives:
Firstly, we aim at deep semantic parsing of video and text that goes beyond the
traditional bag-of-words approaches; Secondly, we perform parsing and reasoning
across the spatial, temporal and causal dimensions based on the joint S/T/C-AOG
representation; Thirdly, we show that deep joint parsing facilitates subsequent
applications such as generating narrative text descriptions and answering
queries in the forms of who, what, when, where and why. We empirically
evaluated our system based on comparison against ground-truth as well as
accuracy of query answering and obtained satisfactory results
Type-driven semantic interpretation and feature dependencies in R-LFG
Once one has enriched LFG's formal machinery with the linear logic mechanisms
needed for semantic interpretation as proposed by Dalrymple et. al., it is
natural to ask whether these make any existing components of LFG redundant. As
Dalrymple and her colleagues note, LFG's f-structure completeness and coherence
constraints fall out as a by-product of the linear logic machinery they propose
for semantic interpretation, thus making those f-structure mechanisms
redundant. Given that linear logic machinery or something like it is
independently needed for semantic interpretation, it seems reasonable to
explore the extent to which it is capable of handling feature structure
constraints as well.
R-LFG represents the extreme position that all linguistically required
feature structure dependencies can be captured by the resource-accounting
machinery of a linear or similiar logic independently needed for semantic
interpretation, making LFG's unification machinery redundant. The goal is to
show that LFG linguistic analyses can be expressed as clearly and perspicuously
using the smaller set of mechanisms of R-LFG as they can using the much larger
set of unification-based mechanisms in LFG: if this is the case then we will
have shown that positing these extra f-structure mechanisms is not
linguistically warranted.Comment: 30 pages, to appear in the the ``Glue Language'' volume edited by
Dalrymple, uses tree-dvips, ipa, epic, eepic, fullnam
- …