684 research outputs found
Morphological Analysis as Classification: an Inductive-Learning Approach
Morphological analysis is an important subtask in text-to-speech conversion,
hyphenation, and other language engineering tasks. The traditional approach to
performing morphological analysis is to combine a morpheme lexicon, sets of
(linguistic) rules, and heuristics to find a most probable analysis. In
contrast we present an inductive learning approach in which morphological
analysis is reformulated as a segmentation task. We report on a number of
experiments in which five inductive learning algorithms are applied to three
variations of the task of morphological analysis. Results show (i) that the
generalisation performance of the algorithms is good, and (ii) that the lazy
learning algorithm IB1-IG performs best on all three tasks. We conclude that
lazy learning of morphological analysis as a classification task is indeed a
viable approach; moreover, it has the strong advantages over the traditional
approach of avoiding the knowledge-acquisition bottleneck, being fast and
deterministic in learning and processing, and being language-independent.Comment: 11 pages, 5 encapsulated postscript figures, uses non-standard NeMLaP
proceedings style nemlap.sty; inputs ipamacs (international phonetic
alphabet) and epsf macro
Letter to Sound Rules for Accented Lexicon Compression
This paper presents trainable methods for generating letter to sound rules
from a given lexicon for use in pronouncing out-of-vocabulary words and as a
method for lexicon compression.
As the relationship between a string of letters and a string of phonemes
representing its pronunciation for many languages is not trivial, we discuss
two alignment procedures, one fully automatic and one hand-seeded which produce
reasonable alignments of letters to phones.
Top Down Induction Tree models are trained on the aligned entries. We show
how combined phoneme/stress prediction is better than separate prediction
processes, and still better when including in the model the last phonemes
transcribed and part of speech information. For the lexicons we have tested,
our models have a word accuracy (including stress) of 78% for OALD, 62% for CMU
and 94% for BRULEX. The extremely high scores on the training sets allow
substantial size reductions (more than 1/20).
WWW site: http://tcts.fpms.ac.be/synthesis/mbrdicoComment: 4 pages 1 figur
Memory-Based Lexical Acquisition and Processing
Current approaches to computational lexicology in language technology are
knowledge-based (competence-oriented) and try to abstract away from specific
formalisms, domains, and applications. This results in severe complexity,
acquisition and reusability bottlenecks. As an alternative, we propose a
particular performance-oriented approach to Natural Language Processing based
on automatic memory-based learning of linguistic (lexical) tasks. The
consequences of the approach for computational lexicology are discussed, and
the application of the approach on a number of lexical acquisition and
disambiguation tasks in phonology, morphology and syntax is described.Comment: 18 page
A Comparison of Different Machine Transliteration Models
Machine transliteration is a method for automatically converting words in one
language into phonetically equivalent ones in another language. Machine
transliteration plays an important role in natural language applications such
as information retrieval and machine translation, especially for handling
proper nouns and technical terms. Four machine transliteration models --
grapheme-based transliteration model, phoneme-based transliteration model,
hybrid transliteration model, and correspondence-based transliteration model --
have been proposed by several researchers. To date, however, there has been
little research on a framework in which multiple transliteration models can
operate simultaneously. Furthermore, there has been no comparison of the four
models within the same framework and using the same data. We addressed these
problems by 1) modeling the four models within the same framework, 2) comparing
them under the same conditions, and 3) developing a way to improve machine
transliteration through this comparison. Our comparison showed that the hybrid
and correspondence-based models were the most effective and that the four
models can be used in a complementary manner to improve machine transliteration
performance
Few-Shot and Zero-Shot Learning for Historical Text Normalization
Historical text normalization often relies on small training datasets. Recent
work has shown that multi-task learning can lead to significant improvements by
exploiting synergies with related datasets, but there has been no systematic
study of different multi-task learning architectures. This paper evaluates
63~multi-task learning configurations for sequence-to-sequence-based historical
text normalization across ten datasets from eight languages, using
autoencoding, grapheme-to-phoneme mapping, and lemmatization as auxiliary
tasks. We observe consistent, significant improvements across languages when
training data for the target task is limited, but minimal or no improvements
when training data is abundant. We also show that zero-shot learning
outperforms the simple, but relatively strong, identity baseline.Comment: Accepted at DeepLo-201
Teaching Phoneme-Grapheme Correspondence Rules to Children With Learning Difficulties : An Implementation of the Fernald Method
A single subject design was used to investigate the effectiveness of the Fernald method as an instructional technique for teaching phoneme-grapheme correspondence rules. Participants were tour primary grade children with learning difficulties in reading. Intervention training focussed on teaching phoneme-grapheme correspondence rules through the implementation of an adapted version of the Fernald method. The two dependent variables were word recognition and fluency rates. The design of the study allowed analysis of maintenance and generalisation of the relevant variables. Results demonstrated an increase in word recognition skills and fluency rates by all four children with learning difficulties. Maintenance of these gains occurred in two children, while the other two children experienced a slight decrease in their word recognition and fluency rates during the two week follow-up probes. The results of this study clearly support the numerous research papers summarised by Adams (1990). Motivation was not measured in this study, but appeared to have a significant influence in the children\u27s results. The classroom implications of these findings are further discussed in the following chapters of this study
Do not forget: Full memory in memory-based learning of word pronunciation
Memory-based learning, keeping full memory of learning material, appears a
viable approach to learning NLP tasks, and is often superior in generalisation
accuracy to eager learning approaches that abstract from learning material.
Here we investigate three partial memory-based learning approaches which remove
from memory specific task instance types estimated to be exceptional. The three
approaches each implement one heuristic function for estimating exceptionality
of instance types: (i) typicality, (ii) class prediction strength, and (iii)
friendly-neighbourhood size. Experiments are performed with the memory-based
learning algorithm IB1-IG trained on English word pronunciation. We find that
removing instance types with low prediction strength (ii) is the only tested
method which does not seriously harm generalisation accuracy. We conclude that
keeping full memory of types rather than tokens, and excluding minority
ambiguities appear to be the only performance-preserving optimisations of
memory-based learning.Comment: uses conll98, epsf, and ipamacs (WSU IPA
Forgetting Exceptions is Harmful in Language Learning
We show that in language learning, contrary to received wisdom, keeping
exceptional training instances in memory can be beneficial for generalization
accuracy. We investigate this phenomenon empirically on a selection of
benchmark natural language processing tasks: grapheme-to-phoneme conversion,
part-of-speech tagging, prepositional-phrase attachment, and base noun phrase
chunking. In a first series of experiments we combine memory-based learning
with training set editing techniques, in which instances are edited based on
their typicality and class prediction strength. Results show that editing
exceptional instances (with low typicality or low class prediction strength)
tends to harm generalization accuracy. In a second series of experiments we
compare memory-based learning and decision-tree learning methods on the same
selection of tasks, and find that decision-tree learning often performs worse
than memory-based learning. Moreover, the decrease in performance can be linked
to the degree of abstraction from exceptions (i.e., pruning or eagerness). We
provide explanations for both results in terms of the properties of the natural
language processing tasks and the learning algorithms.Comment: 31 pages, 7 figures, 10 tables. uses 11pt, fullname, a4wide tex
styles. Pre-print version of article to appear in Machine Learning 11:1-3,
Special Issue on Natural Language Learning. Figures on page 22 slightly
compressed to avoid page overloa
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