842 research outputs found
Principles and Implementation of Deductive Parsing
We present a system for generating parsers based directly on the metaphor of
parsing as deduction. Parsing algorithms can be represented directly as
deduction systems, and a single deduction engine can interpret such deduction
systems so as to implement the corresponding parser. The method generalizes
easily to parsers for augmented phrase structure formalisms, such as
definite-clause grammars and other logic grammar formalisms, and has been used
for rapid prototyping of parsing algorithms for a variety of formalisms
including variants of tree-adjoining grammars, categorial grammars, and
lexicalized context-free grammars.Comment: 69 pages, includes full Prolog cod
An earley parsing algorithm for range concatenation grammars
We present a CYK and an Earley-style algorithm for parsing Range Concatenation Grammar (RCG), using the deductive parsing framework. The characteristic property of the Earley parser is that we use a technique of range boundary constraint propagation to compute the yields of non-terminals as late as possible. Experiments show that, compared to previous approaches, the constraint propagation helps to considerably decrease the number of items in the chart
Grasp: Randomised Semiring Parsing
We present a suite of algorithms for inference tasks over (finite and infinite) context-free sets. For generality and clarity, we have chosen the framework of semiring parsing with support to the most common semirings (e.g. Forest, Viterbi, k-best and Inside). We see parsing from the more general viewpoint of weighted deduction allowing for arbitrary weighted finite-state input and provide implementations of both bottom-up (CKY-inspired) and top-down (Earley-inspired) algorithms. We focus on approximate inference by Monte Carlo methods and provide implementations of ancestral sampling and slice sampling. In principle, sampling methods can deal with models whose independence assumptions are weaker than what is feasible by standard dynamic programming. We envision applications such as monolingual constituency parsing, synchronous parsing, context-free models of reordering for machine translation, and machine translation decoding
Memoization in Constraint Logic Programming
This paper shows how to apply memoization (caching of subgoals and associated
answer substitutions) in a constraint logic programming setting. The research
is is motivated by the desire to apply constraint logic programming (CLP) to
problems in natural language processing that involve (constraint) interleaving
or coroutining, such as GB and HPSG parsing.Comment: 11 page
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
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