2,362 research outputs found
Conditional Random Field Autoencoders for Unsupervised Structured Prediction
We introduce a framework for unsupervised learning of structured predictors
with overlapping, global features. Each input's latent representation is
predicted conditional on the observable data using a feature-rich conditional
random field. Then a reconstruction of the input is (re)generated, conditional
on the latent structure, using models for which maximum likelihood estimation
has a closed-form. Our autoencoder formulation enables efficient learning
without making unrealistic independence assumptions or restricting the kinds of
features that can be used. We illustrate insightful connections to traditional
autoencoders, posterior regularization and multi-view learning. We show
competitive results with instantiations of the model for two canonical NLP
tasks: part-of-speech induction and bitext word alignment, and show that
training our model can be substantially more efficient than comparable
feature-rich baselines
Multilingual domain modeling in Twenty-One: automatic creation of a bi-directional translation lexicon from a parallel corpus
Within the project Twenty-One, which aims at the effective dissemination of information on ecology and sustainable development, a sytem is developed that supports cross-language information retrieval in any of the four languages Dutch, English, French and German. Knowledge of this application domain is needed to enhance existing translation resources for the purpose of lexical disambiguation. This paper describes an algorithm for the automated acquisition of a translation lexicon from a parallel corpus. New about the presented algorithm is the statistical language model used. Because the algorithm is based on a symmetric translation model it becomes possible to identify one-to-many and many-to-one relations between words of a language pair. We claim that the presented method has two advantages over algorithms that have been published before. Firstly, because the translation model is more powerful, the resulting bilingual lexicon will be more accurate. Secondly, the resulting bilingual lexicon can be used to translate in both directions between a language pair. Different versions of the algorithm were evaluated on the Dutch and English version of the Agenda 21 corpus, which is a UN document on the application domain of sustainable development
Induction of Word and Phrase Alignments for Automatic Document Summarization
Current research in automatic single document summarization is dominated by
two effective, yet naive approaches: summarization by sentence extraction, and
headline generation via bag-of-words models. While successful in some tasks,
neither of these models is able to adequately capture the large set of
linguistic devices utilized by humans when they produce summaries. One possible
explanation for the widespread use of these models is that good techniques have
been developed to extract appropriate training data for them from existing
document/abstract and document/headline corpora. We believe that future
progress in automatic summarization will be driven both by the development of
more sophisticated, linguistically informed models, as well as a more effective
leveraging of document/abstract corpora. In order to open the doors to
simultaneously achieving both of these goals, we have developed techniques for
automatically producing word-to-word and phrase-to-phrase alignments between
documents and their human-written abstracts. These alignments make explicit the
correspondences that exist in such document/abstract pairs, and create a
potentially rich data source from which complex summarization algorithms may
learn. This paper describes experiments we have carried out to analyze the
ability of humans to perform such alignments, and based on these analyses, we
describe experiments for creating them automatically. Our model for the
alignment task is based on an extension of the standard hidden Markov model,
and learns to create alignments in a completely unsupervised fashion. We
describe our model in detail and present experimental results that show that
our model is able to learn to reliably identify word- and phrase-level
alignments in a corpus of pairs
An annotation scheme and gold standard for Dutch-English word alignment
The importance of sentence-aligned parallel corpora has been widely acknowledged. Reference corpora in which sub-sentential translational correspondences are indicated manually are more labour-intensive to create, and hence less wide-spread. Such manually created reference alignments - also called Gold Standards - have been used in research projects to develop or test automatic word alignment systems. In most translations, translational correspondences are rather complex; for example word-by-word correspondences can be found only for a limited number of words. A reference corpus in which those complex translational correspondences are aligned manually is therefore also a useful resource for the development of translation tools and for translation studies. In this paper, we describe how we created a Gold Standard for the Dutch-English language pair. We present the annotation scheme, annotation guidelines, annotation tool and inter-annotator results. To cover a wide range of syntactic and stylistic phenomena that emerge from different writing and translation styles, our Gold Standard data set contains texts from different text types. The Gold Standard will be publicly available as part of the Dutch Parallel Corpus
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