658 research outputs found
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
Bootstrapping a Tagged Corpus through Combination of Existing Heterogeneous Taggers
This paper describes a new method, Combi-bootstrap, to exploit existing
taggers and lexical resources for the annotation of corpora with new tagsets.
Combi-bootstrap uses existing resources as features for a second level machine
learning module, that is trained to make the mapping to the new tagset on a
very small sample of annotated corpus material. Experiments show that
Combi-bootstrap: i) can integrate a wide variety of existing resources, and ii)
achieves much higher accuracy (up to 44.7 % error reduction) than both the best
single tagger and an ensemble tagger constructed out of the same small training
sample.Comment: 4 page
Memory-Based Learning: Using Similarity for Smoothing
This paper analyses the relation between the use of similarity in
Memory-Based Learning and the notion of backed-off smoothing in statistical
language modeling. We show that the two approaches are closely related, and we
argue that feature weighting methods in the Memory-Based paradigm can offer the
advantage of automatically specifying a suitable domain-specific hierarchy
between most specific and most general conditioning information without the
need for a large number of parameters. We report two applications of this
approach: PP-attachment and POS-tagging. Our method achieves state-of-the-art
performance in both domains, and allows the easy integration of diverse
information sources, such as rich lexical representations.Comment: 8 pages, uses aclap.sty, To appear in Proc. ACL/EACL 9
Predicting the Effectiveness of Self-Training: Application to Sentiment Classification
The goal of this paper is to investigate the connection between the
performance gain that can be obtained by selftraining and the similarity
between the corpora used in this approach. Self-training is a semi-supervised
technique designed to increase the performance of machine learning algorithms
by automatically classifying instances of a task and adding these as additional
training material to the same classifier. In the context of language processing
tasks, this training material is mostly an (annotated) corpus. Unfortunately
self-training does not always lead to a performance increase and whether it
will is largely unpredictable. We show that the similarity between corpora can
be used to identify those setups for which self-training can be beneficial. We
consider this research as a step in the process of developing a classifier that
is able to adapt itself to each new test corpus that it is presented with
Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resource
Word embeddings have recently seen a strong increase in interest as a result
of strong performance gains on a variety of tasks. However, most of this
research also underlined the importance of benchmark datasets, and the
difficulty of constructing these for a variety of language-specific tasks.
Still, many of the datasets used in these tasks could prove to be fruitful
linguistic resources, allowing for unique observations into language use and
variability. In this paper we demonstrate the performance of multiple types of
embeddings, created with both count and prediction-based architectures on a
variety of corpora, in two language-specific tasks: relation evaluation, and
dialect identification. For the latter, we compare unsupervised methods with a
traditional, hand-crafted dictionary. With this research, we provide the
embeddings themselves, the relation evaluation task benchmark for use in
further research, and demonstrate how the benchmarked embeddings prove a useful
unsupervised linguistic resource, effectively used in a downstream task.Comment: in LREC 201
Unsupervised Discovery of Phonological Categories through Supervised Learning of Morphological Rules
We describe a case study in the application of {\em symbolic machine
learning} techniques for the discovery of linguistic rules and categories. A
supervised rule induction algorithm is used to learn to predict the correct
diminutive suffix given the phonological representation of Dutch nouns. The
system produces rules which are comparable to rules proposed by linguists.
Furthermore, in the process of learning this morphological task, the phonemes
used are grouped into phonologically relevant categories. We discuss the
relevance of our method for linguistics and language technology
Unsupervised Context-Sensitive Spelling Correction of English and Dutch Clinical Free-Text with Word and Character N-Gram Embeddings
We present an unsupervised context-sensitive spelling correction method for
clinical free-text that uses word and character n-gram embeddings. Our method
generates misspelling replacement candidates and ranks them according to their
semantic fit, by calculating a weighted cosine similarity between the
vectorized representation of a candidate and the misspelling context. To tune
the parameters of this model, we generate self-induced spelling error corpora.
We perform our experiments for two languages. For English, we greatly
outperform off-the-shelf spelling correction tools on a manually annotated
MIMIC-III test set, and counter the frequency bias of a noisy channel model,
showing that neural embeddings can be successfully exploited to improve upon
the state-of-the-art. For Dutch, we also outperform an off-the-shelf spelling
correction tool on manually annotated clinical records from the Antwerp
University Hospital, but can offer no empirical evidence that our method
counters the frequency bias of a noisy channel model in this case as well.
However, both our context-sensitive model and our implementation of the noisy
channel model obtain high scores on the test set, establishing a
state-of-the-art for Dutch clinical spelling correction with the noisy channel
model.Comment: Appears in volume 7 of the CLIN Journal,
http://www.clinjournal.org/biblio/volum
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