IEEE Intelligent Systems
Computational Linguistics - Special issue on web as corpus
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Identifying word translations in non-parallel texts
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Automatic identification of word translations from unrelated English and German corpora
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Improving Machine Translation Performance by Exploiting Non-Parallel Corpora
Computational Linguistics
Extracting parallel sub-sentential fragments from non-parallel corpora
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
The second release of the RASP system
COLING-ACL '06 Proceedings of the COLING/ACL on Interactive presentation sessions
Adaptive, intelligent presentation of information for the museum visitor in PEACH
User Modeling and User-Adapted Interaction
Automatic generation of textual summaries from neonatal intensive care data
Artificial Intelligence
System building cost vs. output quality in data-to-text generation
ENLG '09 Proceedings of the 12th European Workshop on Natural Language Generation
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In this paper we investigate automatic data-text alignment, i.e. the task of automatically aligning data records with textual descriptions, such that data tokens are aligned with the word strings that describe them. Our methods make use of log likelihood ratios to estimate the strength of association between data tokens and text tokens. We investigate data-text alignment at the document level and at the sentence level, reporting results for several methodological variants as well as baselines. We find that log likelihood ratios provide a strong basis for predicting data-text alignment.