6,298 research outputs found
Learning Language from a Large (Unannotated) Corpus
A novel approach to the fully automated, unsupervised extraction of
dependency grammars and associated syntax-to-semantic-relationship mappings
from large text corpora is described. The suggested approach builds on the
authors' prior work with the Link Grammar, RelEx and OpenCog systems, as well
as on a number of prior papers and approaches from the statistical language
learning literature. If successful, this approach would enable the mining of
all the information needed to power a natural language comprehension and
generation system, directly from a large, unannotated corpus.Comment: 29 pages, 5 figures, research proposa
All mixed up? Finding the optimal feature set for general readability prediction and its application to English and Dutch
Readability research has a long and rich tradition, but there has been too little focus on general readability prediction without targeting a specific audience or text genre. Moreover, though NLP-inspired research has focused on adding more complex readability features there is still no consensus on which features contribute most to the prediction. In this article, we investigate in close detail the feasibility of constructing a readability prediction system for English and Dutch generic text using supervised machine learning. Based on readability assessments by both experts
and a crowd, we implement different types of text characteristics ranging from easy-to-compute superficial text characteristics to features requiring a deep linguistic processing, resulting in ten
different feature groups. Both a regression and classification setup are investigated reflecting the two possible readability prediction tasks: scoring individual texts or comparing two texts. We show that going beyond correlation calculations for readability optimization using a wrapper-based genetic algorithm optimization approach is a promising task which provides considerable insights in which feature combinations contribute to the overall readability prediction. Since we also have gold standard information available for those features requiring deep processing we are able to investigate the true upper bound of our Dutch system. Interestingly, we will observe that the performance of our fully-automatic readability prediction pipeline is on par with the pipeline using golden deep syntactic and semantic information
Assessing the Usefulness of Different Feature Sets for Predicting the Comprehension Difficulty of Text
Within English second language acquisition there is an enthusiasm for using authentic text as learning materials in classroom and online settings. This enthusiasm, however, is tempered by the difficulty in finding authentic texts at suitable levels of comprehension difficulty for specific groups of learners. An automated way to rate the comprehension difficulty of a text would make finding suitable texts a much more manageable task. While readability metrics have been in use for over 50 years now they only capture a small amount of what constitutes comprehension difficulty. In this paper we examine other features of texts that are related to comprehension difficulty and assess their usefulness in building automated prediction models. We investigate readability metrics, vocabulary-based features, and syntax-based features, and show that the best prediction accuracies are possible with a combination of all three
Discourse Structure in Machine Translation Evaluation
In this article, we explore the potential of using sentence-level discourse
structure for machine translation evaluation. We first design discourse-aware
similarity measures, which use all-subtree kernels to compare discourse parse
trees in accordance with the Rhetorical Structure Theory (RST). Then, we show
that a simple linear combination with these measures can help improve various
existing machine translation evaluation metrics regarding correlation with
human judgments both at the segment- and at the system-level. This suggests
that discourse information is complementary to the information used by many of
the existing evaluation metrics, and thus it could be taken into account when
developing richer evaluation metrics, such as the WMT-14 winning combined
metric DiscoTKparty. We also provide a detailed analysis of the relevance of
various discourse elements and relations from the RST parse trees for machine
translation evaluation. In particular we show that: (i) all aspects of the RST
tree are relevant, (ii) nuclearity is more useful than relation type, and (iii)
the similarity of the translation RST tree to the reference tree is positively
correlated with translation quality.Comment: machine translation, machine translation evaluation, discourse
analysis. Computational Linguistics, 201
Improving the translation environment for professional translators
When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side.
This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project
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