118 research outputs found
From alternations to ordered rules : a system for learning derivational phonology
This work presents a computational rule learner tasked with inferring underlying forms and ordered rules from phonological paradigms akin to those found in traditional pen and paper analyses. The scheme being proposed is a batch learner capable of analysing surface alternations and hypothesising ordered derivations compatible with them in order to create an explicit mapping from UR to SR. We shall refer to both the competence of an idealised speaker-hearer (in keeping with traditional generative linguistic theory) and the conscious methods employed by the phonologist in the course of analysing data sets. The fundamental axiom of this model is that the child has memorised the relevant surface forms (as they appear in the paradigm) alongside the appropriate semantic information in order to allow them to set up paradigmatic structures for the purpose of inferring both underlying forms and phonological rules simultaneously. The mapping from minimal pairs to underlying forms is the primary conduit to inferring the rules themselves
Darstellung und stochastische Auflösung von Ambiguität in constraint-basiertem Parsing
Diese Arbeit untersucht zwei komplementäre Ansätze zum Umgang mit Mehrdeutigkeiten bei der automatischen Verarbeitung natürlicher Sprache. Zunächst werden Methoden vorgestellt, die es erlauben, viele konkurrierende Interpretationen in einer gemeinsamen Datenstruktur kompakt zu repräsentieren. Dann werden Ansätze vorgeschlagen, die verschiedenen Interpretationen mit Hilfe von stochastischen Modellen zu bewerten. Für das dabei auftretende Problem, Wahrscheinlichkeiten von seltenen Ereignissen zu schätzen, die in den Trainingsdaten nicht auftraten, werden neuartige Methoden vorgeschlagen.This thesis investigates two complementary approches to cope with ambiguities in natural language processing. It first presents methods that allow to store many competing interpretations compactly in one shared datastructure. It then suggests approaches to score the different interpretations using stochastic models. This leads to the problem of estimation of probabilities of rare events that have not been observed in the training data, for which novel methods are proposed
Handbook of Lexical Functional Grammar
Lexical Functional Grammar (LFG) is a nontransformational theory of
linguistic structure, first developed in the 1970s by Joan Bresnan and
Ronald M. Kaplan, which assumes that language is best described and
modeled by parallel structures representing different facets of
linguistic organization and information, related by means of
functional correspondences. This volume has five parts. Part I,
Overview and Introduction, provides an introduction to core syntactic
concepts and representations. Part II, Grammatical Phenomena, reviews
LFG work on a range of grammatical phenomena or constructions. Part
III, Grammatical modules and interfaces, provides an overview of LFG
work on semantics, argument structure, prosody, information structure,
and morphology. Part IV, Linguistic disciplines, reviews LFG work in
the disciplines of historical linguistics, learnability,
psycholinguistics, and second language learning. Part V, Formal and
computational issues and applications, provides an overview of
computational and formal properties of the theory, implementations,
and computational work on parsing, translation, grammar induction, and
treebanks. Part VI, Language families and regions, reviews LFG work
on languages spoken in particular geographical areas or in particular
language families. The final section, Comparing LFG with other
linguistic theories, discusses LFG work in relation to other
theoretical approaches
Probabilistic grammar induction from sentences and structured meanings
The meanings of natural language sentences may be represented as compositional
logical-forms. Each word or lexicalised multiword-element has an associated logicalform
representing its meaning. Full sentential logical-forms are then composed from
these word logical-forms via a syntactic parse of the sentence.
This thesis develops two computational systems that learn both the word-meanings
and parsing model required to map sentences onto logical-forms from an example corpus
of (sentence, logical-form) pairs. One of these systems is designed to provide a
general purpose method of inducing semantic parsers for multiple languages and logical
meaning representations. Semantic parsers map sentences onto logical representations
of their meanings and may form an important part of any computational task that
needs to interpret the meanings of sentences. The other system is designed to model
the way in which a child learns the semantics and syntax of their first language. Here,
logical-forms are used to represent the potentially ambiguous context in which childdirected
utterances are spoken and a psycholinguistically plausible training algorithm
learns a probabilistic grammar that describes the target language. This computational
modelling task is important as it can provide evidence for or against competing theories
of how children learn their first language.
Both of the systems presented here are based upon two working hypotheses. First,
that the correct parse of any sentence in any language is contained in a set of possible
parses defined in terms of the sentence itself, the sentence’s logical-form and a small
set of combinatory rule schemata. The second working hypothesis is that, given a
corpus of (sentence, logical-form) pairs that each support a large number of possible
parses according to the schemata mentioned above, it is possible to learn a probabilistic
parsing model that accurately describes the target language.
The algorithm for semantic parser induction learns Combinatory Categorial Grammar
(CCG) lexicons and discriminative probabilistic parsing models from corpora of
(sentence, logical-form) pairs. This system is shown to achieve at or near state of the art
performance across multiple languages, logical meaning representations and domains.
As the approach is not tied to any single natural or logical language, this system represents
an important step towards widely applicable black-box methods for semantic parser induction. This thesis also develops an efficient representation of the CCG lexicon
that separately stores language specific syntactic regularities and domain specific
semantic knowledge. This factorised lexical representation improves the performance
of CCG based semantic parsers in sparse domains and also provides a potential basis
for lexical expansion and domain adaptation for semantic parsers.
The algorithm for modelling child language acquisition learns a generative probabilistic
model of CCG parses from sentences paired with a context set of potential
logical-forms containing one correct entry and a number of distractors. The online
learning algorithm used is intended to be psycholinguistically plausible and to assume
as little information specific to the task of language learning as is possible. It is shown
that this algorithm learns an accurate parsing model despite making very few initial
assumptions. It is also shown that the manner in which both word-meanings and syntactic
rules are learnt is in accordance with observations of both of these learning tasks
in children, supporting a theory of language acquisition that builds upon the two working
hypotheses stated above
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
Wide-coverage statistical parsing with minimalist grammars
Syntactic parsing is the process of automatically assigning a structure to a string
of words, and is arguably a necessary prerequisite for obtaining a detailed and precise
representation of sentence meaning. For many NLP tasks, it is sufficient to use
parsers based on simple context free grammars. However, for tasks in which precision
on certain relatively rare but semantically crucial constructions (such as unbounded
wh-movements for open domain question answering) is important, more expressive
grammatical frameworks still have an important role to play.
One grammatical framework which has been conspicuously absent from journals
and conferences on Natural Language Processing (NLP), despite continuing to dominate
much of theoretical syntax, is Minimalism, the latest incarnation of the Transformational
Grammar (TG) approach to linguistic theory developed very extensively
by Noam Chomsky and many others since the early 1950s. Until now, all parsers
using genuine transformational movement operations have had only narrow coverage
by modern standards, owing to the lack of any wide-coverage TG grammars or treebanks
on which to train statistical models. The received wisdom within NLP is that
TG is too complex and insufficiently formalised to be applied to realistic parsing tasks.
This situation is unfortunate, as it is arguably the most extensively developed syntactic
theory across the greatest number of languages, many of which are otherwise
under-resourced, and yet the vast majority of its insights never find their way into NLP
systems. Conversely, the process of constructing large grammar fragments can have
a salutary impact on the theory itself, forcing choices between competing analyses of
the same construction, and exposing incompatibilities between analyses of different
constructions, along with areas of over- and undergeneration which may otherwise go
unnoticed.
This dissertation builds on research into computational Minimalism pioneered by
Ed Stabler and others since the late 1990s to present the first ever wide-coverage Minimalist
Grammar (MG) parser, along with some promising initial experimental results.
A wide-coverage parser must of course be equipped with a wide-coverage grammar,
and this dissertation will therefore also present the first ever wide-coverage MG, which
has analyses with a high level of cross-linguistic descriptive adequacy for a great many
English constructions, many of which are taken or adapted from proposals in the mainstream
Minimalist literature. The grammar is very deep, in the sense that it describes
many long-range dependencies which even most other expressive wide-coverage grammars
ignore. At the same time, it has also been engineered to be highly constrained,
with continuous computational testing being applied to minimize both under- and over-generation.
Natural language is highly ambiguous, both locally and globally, and even with a
very strong formal grammar, there may still be a great many possible structures for a
given sentence and its substrings. The standard approach to resolving such ambiguity
is to equip the parser with a probability model allowing it to disregard certain unlikely
search paths, thereby increasing both its efficiency and accuracy. The most successful
parsing models are those extracted in a supervised fashion from labelled data in the
form of a corpus of syntactic trees, known as a treebank. Constructing such a treebank
from scratch for a different formalism is extremely time-consuming and expensive,
however, and so the standard approach is to map the trees in an existing treebank into
trees of the target formalism. Minimalist trees are considerably more complex than
those of other formalisms, however, containing many more null heads and movement
operations, making this conversion process far from trivial. This dissertation will describe
a method which has so far been used to convert 56% of the Penn Treebank trees
into MG trees. Although still under development, the resulting MGbank corpus has
already been used to train a statistical A* MG parser, described here, which has an
expected asymptotic time complexity of O(n3); this is much better than even the most
optimistic worst case analysis for the formalism
Integrated bilingual grammatical architecture: Insights from syntactic development
It is generally agreed upon today that bilingual children are able to differentiate their two languages as early as the babbling stage, but that the child is able to make such a distinction does not entail that the grammar does in the same categorical way. This dissertation argues that bilingual grammar is integrated rather than isolated, on the basis of evidence of cross-linguistic influences in syntactic development: positive cross-linguistic influence, or ‘facilitation’, is captured within the same system as negative cross-linguistic influence, or ‘interference’. In analyzing the phenomena in Optimality Theory—a framework of universal, violable grammatical constraints—I show how an integrated bilingual grammatical architecture can explain those phenomena, which reflect a variety of structural representations, as arising from a grammar that does not fundamentally differ from a monolingual one. The empirical focus of the dissertation is on Spanish-English bilingual data from two experiments and from corpora of spontaneous speech, on the basis of which three main types of constructions are studied: predicative sentences involving BE verbs, wh-questions, and noun modification. Taking the traditional characterization of an Optimality-Theoretic grammar as a point of departure, each analyzed construction poses a new challenge to the architecture. The notions of ‘language tags’ within the propositional representation of an individual utterance and ‘split- and-tagged constraints’ that utilize those propositions’ tags in evaluating their own applicability are introduced in response to those challenges, as is a novel account of the cross-linguistic influences that can be elicited in real time. Implications for the architecture of the bilingual adult grammar are also discussed
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