6,811 research outputs found
A Machine learning approach to POS tagging
We have applied inductive learning of statistical decision trees
and relaxation labelling to the Natural Language Processing (NLP)
task of morphosyntactic disambiguation (Part Of Speech Tagging).
The learning process is supervised and obtains a language
model oriented to resolve POS ambiguities. This model consists
of a set of statistical decision trees expressing distribution of
tags and words in some relevant contexts.
The acquired language models are complete enough to be directly
used as sets of POS disambiguation rules, and include more complex
contextual information than simple collections of n-grams usually
used in statistical taggers.
We have implemented a quite simple and fast tagger that has been
tested and evaluated on the Wall Street Journal (WSJ) corpus with
a remarkable accuracy.
However, better results can be obtained by translating the trees
into rules to feed a flexible relaxation labelling based tagger.
In this direction we describe a tagger which is able to use
information of any kind (n-grams, automatically acquired constraints,
linguistically motivated manually written constraints, etc.), and in
particular to incorporate the machine learned decision trees.
Simultaneously, we address the problem of tagging when only
small training material is available, which is crucial in any process
of constructing, from scratch, an annotated corpus. We show that quite
high accuracy can be achieved with our system in this situation.Postprint (published version
MBT: A Memory-Based Part of Speech Tagger-Generator
We introduce a memory-based approach to part of speech tagging. Memory-based
learning is a form of supervised learning based on similarity-based reasoning.
The part of speech tag of a word in a particular context is extrapolated from
the most similar cases held in memory. Supervised learning approaches are
useful when a tagged corpus is available as an example of the desired output of
the tagger. Based on such a corpus, the tagger-generator automatically builds a
tagger which is able to tag new text the same way, diminishing development time
for the construction of a tagger considerably. Memory-based tagging shares this
advantage with other statistical or machine learning approaches. Additional
advantages specific to a memory-based approach include (i) the relatively small
tagged corpus size sufficient for training, (ii) incremental learning, (iii)
explanation capabilities, (iv) flexible integration of information in case
representations, (v) its non-parametric nature, (vi) reasonably good results on
unknown words without morphological analysis, and (vii) fast learning and
tagging. In this paper we show that a large-scale application of the
memory-based approach is feasible: we obtain a tagging accuracy that is on a
par with that of known statistical approaches, and with attractive space and
time complexity properties when using {\em IGTree}, a tree-based formalism for
indexing and searching huge case bases.} The use of IGTree has as additional
advantage that optimal context size for disambiguation is dynamically computed.Comment: 14 pages, 2 Postscript figure
Structured Training for Neural Network Transition-Based Parsing
We present structured perceptron training for neural network transition-based
dependency parsing. We learn the neural network representation using a gold
corpus augmented by a large number of automatically parsed sentences. Given
this fixed network representation, we learn a final layer using the structured
perceptron with beam-search decoding. On the Penn Treebank, our parser reaches
94.26% unlabeled and 92.41% labeled attachment accuracy, which to our knowledge
is the best accuracy on Stanford Dependencies to date. We also provide in-depth
ablative analysis to determine which aspects of our model provide the largest
gains in accuracy
A New Statistical Parser Based on Bigram Lexical Dependencies
This paper describes a new statistical parser which is based on probabilities
of dependencies between head-words in the parse tree. Standard bigram
probability estimation techniques are extended to calculate probabilities of
dependencies between pairs of words. Tests using Wall Street Journal data show
that the method performs at least as well as SPATTER (Magerman 95, Jelinek et
al 94), which has the best published results for a statistical parser on this
task. The simplicity of the approach means the model trains on 40,000 sentences
in under 15 minutes. With a beam search strategy parsing speed can be improved
to over 200 sentences a minute with negligible loss in accuracy.Comment: 8 pages, to appear in Proceedings of ACL 96. Uuencoded gz-compressed
postscript file created by csh script uufile
Tagging Complex Non-Verbal German Chunks with Conditional Random Fields
We report on chunk tagging methods for German that recognize complex non-verbal phrases using structural chunk tags with Conditional Random Fields (CRFs). This state-of-the-art method for sequence classification achieves 93.5% accuracy on newspaper text. For the same task, a classical trigram tagger approach based on Hidden Markov Models reaches a baseline of 88.1%. CRFs allow for a clean and principled integration of linguistic knowledge such as part-of-speech tags, morphological constraints and lemmas. The structural chunk tags encode phrase structures up to a depth of 3 syntactic nodes. They include complex prenominal and postnominal modifiers that occur frequently in German noun phrases
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