224 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
Macro Grammars and Holistic Triggering for Efficient Semantic Parsing
To learn a semantic parser from denotations, a learning algorithm must search
over a combinatorially large space of logical forms for ones consistent with
the annotated denotations. We propose a new online learning algorithm that
searches faster as training progresses. The two key ideas are using macro
grammars to cache the abstract patterns of useful logical forms found thus far,
and holistic triggering to efficiently retrieve the most relevant patterns
based on sentence similarity. On the WikiTableQuestions dataset, we first
expand the search space of an existing model to improve the state-of-the-art
accuracy from 38.7% to 42.7%, and then use macro grammars and holistic
triggering to achieve an 11x speedup and an accuracy of 43.7%.Comment: EMNLP 201
An Abstract Machine for Unification Grammars
This work describes the design and implementation of an abstract machine,
Amalia, for the linguistic formalism ALE, which is based on typed feature
structures. This formalism is one of the most widely accepted in computational
linguistics and has been used for designing grammars in various linguistic
theories, most notably HPSG. Amalia is composed of data structures and a set of
instructions, augmented by a compiler from the grammatical formalism to the
abstract instructions, and a (portable) interpreter of the abstract
instructions. The effect of each instruction is defined using a low-level
language that can be executed on ordinary hardware.
The advantages of the abstract machine approach are twofold. From a
theoretical point of view, the abstract machine gives a well-defined
operational semantics to the grammatical formalism. This ensures that grammars
specified using our system are endowed with well defined meaning. It enables,
for example, to formally verify the correctness of a compiler for HPSG, given
an independent definition. From a practical point of view, Amalia is the first
system that employs a direct compilation scheme for unification grammars that
are based on typed feature structures. The use of amalia results in a much
improved performance over existing systems.
In order to test the machine on a realistic application, we have developed a
small-scale, HPSG-based grammar for a fragment of the Hebrew language, using
Amalia as the development platform. This is the first application of HPSG to a
Semitic language.Comment: Doctoral Thesis, 96 pages, many postscript figures, uses pstricks,
pst-node, psfig, fullname and a macros fil
Natural Language Processing
The subject of Natural Language Processing can be considered in both broad and narrow senses. In the broad sense, it covers processing issues at all levels of natural language understanding, including speech recognition, syntactic and semantic analysis of sentences, reference to the discourse context (including anaphora, inference of referents, and more extended relations of discourse coherence and narrative structure), conversational inference and implicature, and discourse planning and generation. In the narrower sense, it covers the syntactic and semantic processing sentences to deliver semantic objects suitable for referring, inferring, and the like. Of course, the results of inference and reference may under some circumstances play a part in processing in the narrow sense. But the processes that are characteristic of these other modules are not the primary concern
A Computational Model of Syntactic Processing: Ambiguity Resolution from Interpretation
Syntactic ambiguity abounds in natural language, yet humans have no
difficulty coping with it. In fact, the process of ambiguity resolution is
almost always unconscious. But it is not infallible, however, as example 1
demonstrates.
1. The horse raced past the barn fell.
This sentence is perfectly grammatical, as is evident when it appears in the
following context:
2. Two horses were being shown off to a prospective buyer. One was raced past
a meadow. and the other was raced past a barn. ...
Grammatical yet unprocessable sentences such as 1 are called `garden-path
sentences.' Their existence provides an opportunity to investigate the human
sentence processing mechanism by studying how and when it fails. The aim of
this thesis is to construct a computational model of language understanding
which can predict processing difficulty. The data to be modeled are known
examples of garden path and non-garden path sentences, and other results from
psycholinguistics.
It is widely believed that there are two distinct loci of computation in
sentence processing: syntactic parsing and semantic interpretation. One
longstanding controversy is which of these two modules bears responsibility for
the immediate resolution of ambiguity. My claim is that it is the latter, and
that the syntactic processing module is a very simple device which blindly and
faithfully constructs all possible analyses for the sentence up to the current
point of processing. The interpretive module serves as a filter, occasionally
discarding certain of these analyses which it deems less appropriate for the
ongoing discourse than their competitors.
This document is divided into three parts. The first is introductory, and
reviews a selection of proposals from the sentence processing literature. The
second part explores a body of data which has been adduced in support of a
theory of structural preferences --- one that is inconsistent with the present
claim. I show how the current proposal can be specified to account for the
available data, and moreover to predict where structural preference theories
will go wrong. The third part is a theoretical investigation of how well the
proposed architecture can be realized using current conceptions of linguistic
competence. In it, I present a parsing algorithm and a meaning-based ambiguity
resolution method.Comment: 128 pages, LaTeX source compressed and uuencoded, figures separate
macros: rotate.sty, lingmacros.sty, psfig.tex. Dissertation, Computer and
Information Science Dept., October 199
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